Associations Between Longitudinal Trajectories of Cognitive and Social Activities and Brain Health in Old Age

Anatürk, Melis; Suri, Sana; Zsoldos, Enikő; Filippini, Nicola; Mahmood, Abda; Singh‐Manoux, Archana; Kivimäki, Mika; Mackay, Clare E.; Ebmeier, Klaus P.; Sexton, Claire E.

doi:10.1001/jamanetworkopen.2020.13793

Cited by 16 publications

(10 citation statements)

References 49 publications

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“…Another study on community-dwelling individuals found that those with higher tongue pressures were more socially active than those with lower tongue pressures [ 19 ]. On the other hand, social activities for the elderly reduced their risk of cognitive decline [ 32 ]. Thus, the deterioration of oral function might be associated with cognitive decline resulting from decreased rates of activity.…”

Section: Discussionmentioning

confidence: 99%

Low Tongue Strength and the Number of Teeth Present Are Associated with Cognitive Decline in Older Japanese Dental Outpatients: A Cross-Sectional Study

Egashira

Mizutani

Yamaguchi

et al. 2020

IJERPH

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To mitigate the impact of dementia, initiating early intervention is important. This study aims to investigate the associations between deterioration in oral function and cognitive decline in older outpatients whose oral health was maintained in the dental clinic. This study included 50 outpatients aged ≥65 years. We used the Japanese version of the Montreal Cognitive Assessment (MoCA-J) to assess cognitive decline. Oral function was evaluated by tongue pressure, masticatory performance, and swallowing ability. A full-mouth periodontal examination was conducted, and the occlusal support and number of teeth were recorded. Odds ratios (ORs) and 95% confidence intervals (CIs) for cognitive decline (MoCA-J score ≤25 points) were calculated using logistic regression models. The age, number of teeth, tongue pressure, and masticatory performance were significantly correlated with cognitive decline (p < 0.05). Logistic regression analyses revealed that cognitive decline was independently associated with age (OR: 1.25; 95% CI: 1.03–1.52; p = 0.024), number of teeth (OR = 0.83; 95% CI: 0.76–1.00; p = 0.047), and lower tongue pressure (OR: 0.87; 95% CI: 0.77–0.98; p = 0.022). Lower tongue pressure and a small number of remaining teeth may be associated with cognitive decline in Japanese outpatients.

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Section: Discussionmentioning

confidence: 99%

Low Tongue Strength and the Number of Teeth Present Are Associated with Cognitive Decline in Older Japanese Dental Outpatients: A Cross-Sectional Study

Egashira

Mizutani

Yamaguchi

et al. 2020

IJERPH

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“…WM features including global and tract‐specific estimates of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and mode of anisotropy (MO) were derived using 48 standard‐space masks available from the ICBM‐DTI‐81 White‐Matter Labels Atlas (Mori, Wakana, van Zijl, & Nagae‐Poetscher, 2005; Wakana et al, 2007). Prior to this step, the diffusion‐weighted images were preprocessed using a combination of FSL's tools, including Top Up to correct for eddy currents and head motion, DTIFIT to fit the diffusion tensor and extract images for each metric of interest (e.g., FA and RD images), which was followed by tract‐based spatial statistics for registration and extracting FA/MD/AD/RD/MO skeletons for each individual (for further details, see supplementary methods in Anatürk et al (2020). Global WM hyperintensity (WMH) volumes were automatically extracted from FLAIR images with Brain Intensity AbNormality Classification Algorithm, as described in Griffanti et al (2016).…”

Section: Methodsmentioning

confidence: 99%

“…However, summarizing measures across brain regions and cognitive domains cannot provide information about specialized cognitive networks potentially linked to regional brain characteristics. Combined with models that estimate regional brain aging patterns (de Lange, Anatürk, et al, 2020;Eavani et al, 2018;Kaufmann et al, 2019;Smith et al, 2020), a development of processspecific cognitive models may provide a more detailed and accurate estimate of the relationship between brain and cognitive maintenance. Furthermore, dynamic brain processes that are not captured by structural measures may play an important role in reserve mechanisms (Franzmeier et al, 2017;Sala-Llonch et al, 2012).…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging

Anatürk

Kaufmann

Cole

et al. 2020

Human Brain Mapping

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The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging‐related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no “gold standard” for measuring these constructs. Using machine‐learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34–82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.

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“…White-Matter Labels Atlas (Mori, Wakana, van Zijl, & Nagae-Poetscher, 2005;Wakana et al, 2007). Prior to this step, the diffusion-weighted images were preprocessed using a combination of FSL's tools, including Top Up to correct for eddy currents and head motion, DTIFIT to fit the diffusion tensor and extract images for each metric of interest (e.g., FA and RD images), which was followed by tract-based spatial statistics for registration and extracting FA/MD/ AD/RD/MO skeletons for each individual (for further details, see supplementary methods in Anatürk et al (2020). Global WM hyperintensity (WMH) volumes were automatically extracted from FLAIR images with Brain Intensity AbNormality Classification Algorithm, as described in Griffanti et al (2016).…”

Section: Mri Featuresmentioning

confidence: 99%

Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging

Anaturk¹,

Kaufmann²,

Cole³

et al. 2020

Preprint

Self Cite

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The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging-related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no 'gold standard' for measuring these constructs. Using machine-learning, we estimated brain and cognitive maintenance based on deviations from normative aging patterns in the Whitehall II MRI sub-study cohort, and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to cognitive maintenance independent of the degree of brain maintenance. No strong evidence was found for associations between lifestyle trajectories and brain or cognitive maintenance. In conclusion, we present a novel method to characterize brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.

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Associations Between Longitudinal Trajectories of Cognitive and Social Activities and Brain Health in Old Age

Abstract: This cohort study examines trajectories of cognitive and social activities from midlife to late life and evaluates whether these trajectories are associated with brain structure, functional connectivity, and cognition.

Cited by 16 publications

References 49 publications

Low Tongue Strength and the Number of Teeth Present Are Associated with Cognitive Decline in Older Japanese Dental Outpatients: A Cross-Sectional Study

Low Tongue Strength and the Number of Teeth Present Are Associated with Cognitive Decline in Older Japanese Dental Outpatients: A Cross-Sectional Study

Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging

Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging

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