Associations between data-driven lifestyle profiles and cognitive function in the AusDiab study

Dingle, Sara E; Bowe, Steven J.; Bujtor, Melissa; Milte, Catherine M.; Daly, Robin M.; Anstey, Kaarin J.; Shaw, Jonathan E.; Torres, Susan

doi:10.1186/s12889-022-14379-z

Cited by 4 publications

(9 citation statements)

References 45 publications

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“…We then examined the association of our three‐profile solution with the initial level of cognitive function and cognitive decline over time. Compared to participants with high engagement in lifestyle health behaviors, those with moderate and (separately) low engagement had steeper annual rates of cognitive decline over time, a finding consistent with the earlier studies 36,37 . However, the findings for the moderate group should be interpreted cautiously due to the borderline significant result.…”

Section: Discussionsupporting

confidence: 82%

“…Compared to participants with high engagement in lifestyle health behaviors, those with moderate and (separately) low engagement had steeper annual rates of cognitive decline over time, a finding consistent with the earlier studies. 36,37 However, the findings for the moderate group should be interpreted cautiously due to the borderline significant result. As such, the difference in cognitive trajectories between the moderate and high groups was much less pronounced compared to the difference between the low and high groups.…”

Section: Discussionmentioning

confidence: 93%

“…36 Overall, studies found that higher engagement in cognitive, physical, and "going out" activities were related to better cognitive function, 37 while not engaging in unhealthy behaviors like smoking. 36 These studies suggested usefulness of employing a latent profile approach to examine engagement in multiple lifestyle health…”

Section: Introductionmentioning

confidence: 99%

“…Recently, studies have used a latent profile or class approach to examine combinations of lifestyle health behaviors and examined relation of profiles or classes to cognitive function in cross‐sectional designs 36,37 or dementia incidence 38 in older adults. These three studies included behaviors that could be promoted or increased to support a healthy lifestyle, though one also included smoking and alcohol consumption 36 . Overall, studies found that higher engagement in cognitive, physical, and “going out” activities were related to better cognitive function, 37 while not engaging in unhealthy behaviors like smoking 36 .…”

Section: Introductionmentioning

confidence: 99%

“…These three studies included behaviors that could be promoted or increased to support a healthy lifestyle, though one also included smoking and alcohol consumption 36 . Overall, studies found that higher engagement in cognitive, physical, and “going out” activities were related to better cognitive function, 37 while not engaging in unhealthy behaviors like smoking 36 . These studies suggested usefulness of employing a latent profile approach to examine engagement in multiple lifestyle health behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Profiles of lifestyle health behaviors and cognitive decline in older adults

Halloway,

Wagner,

Tangney

et al. 2023

Alzheimer's & Dementia

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INTRODUCTIONWe aimed to identify profiles of modifiable, late‐life lifestyle health behaviors related to subsequent maintenance of cognition and explore sociodemographics and health characteristics as effect modifiers.METHODSAnalyses used data from 715 older adults without baseline dementia from the Rush Memory and Aging Project and with lifestyle health behaviors (physical activity, cognitive activity, healthy diet, social activity) at baseline and ≥ 2 annual assessments of cognition. We used latent profile analysis to group participants based on behavior patterns and assessed change in cognition by group.RESULTSThree latent profiles were identified: high (n = 183), moderate (n = 441), and low (n = 91) engagement in health behaviors. Compared to high engagement, the moderate (mean difference [MD] = ‐0.02, 95% CI = [‐0.03;‐0.0002], p = 0.048) and low (MD = ‐0.06, 95% CI = [‐0.08;‐0.03], p < 0.0001) groups had faster annual rates of decline in global cognition, with no significant effects modifiers (vascular risk factors, apolipoprotein E [APOE] ε4, motor function).DISCUSSIONAvoiding low levels of lifestyle health behaviors may help maintain cognition.Highlights Latent profile analysis (LPA) captures lifestyle health behaviors associated with cognitive function. Such behavior include physical activity, cognitive activity, healthy diet, social activity. We used LPA to examine associations of behaviors and cognitive function over time. Older adults with low lifestyle health behaviors showed more rapid decline. To a lesser degree, so did those with moderate lifestyle health behaviors. Vascular conditions and risks, APOEε4, or motor function did not modify the effect.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Profiles of lifestyle health behaviors and cognitive decline in older adults

Halloway,

Wagner,

Tangney

et al. 2023

Alzheimer's & Dementia

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Empirically derived psychosocial‐behavioral phenotypes in Black/African American and Hispanic/Latino older adults enrolled in HABS‐HD: Associations with AD biomarkers and cognitive outcomes

Clark,

Thomas,

Ortega

et al. 2023

Alzheimer's & Dementia

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INTRODUCTIONIdentification of psychosocial‐behavioral phenotypes to understand within‐group heterogeneity in risk and resiliency to Alzheimer's disease (AD) within Black/African American and Hispanic/Latino older adults is essential for the implementation of precision health approaches.METHODSA cluster analysis was performed on baseline measures of socioeconomic resources (annual income, social support, occupational complexity) and psychiatric distress (chronic stress, depression, anxiety) for 1220 racially/ethnically minoritized adults enrolled in the Health and Aging Brain Study‐Health Disparities (HABS‐HD). Analyses of covariance adjusting for sociodemographic factors examined phenotype differences in cognition and plasma AD biomarkers.RESULTSThe cluster analysis identified (1) Low Resource/High Distress (n = 256); (2) High Resource/Low Distress (n = 485); and (3) Low Resource/Low Distress (n = 479) phenotypes. The Low Resource/High Distress phenotype displayed poorer cognition and higher plasma neurofilament light chain; differences between the High Resource/Low Distress and Low Resource/Low Distress phenotypes were minimal.DISCUSSIONThe identification of psychosocial‐behavioral phenotypes within racially/ethnically minoritized older adults is crucial to the development of targeted AD prevention and intervention efforts.

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The role of perfusion, grey matter and behavioural phenotypes in the data-driven classification of cognitive syndromes

Vipin,

Lee,

Kumar

et al. 2023

Preprint

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BACKGROUND The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach. METHODS Study participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained. RESULTS 373 participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637). CONCLUSIONS Brain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes.

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Associations between data-driven lifestyle profiles and cognitive function in the AusDiab study

Cited by 4 publications

References 45 publications

Profiles of lifestyle health behaviors and cognitive decline in older adults

Profiles of lifestyle health behaviors and cognitive decline in older adults

Empirically derived psychosocial‐behavioral phenotypes in Black/African American and Hispanic/Latino older adults enrolled in HABS‐HD: Associations with AD biomarkers and cognitive outcomes

The role of perfusion, grey matter and behavioural phenotypes in the data-driven classification of cognitive syndromes

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