Identifying a group of factors predicting cognitive impairment among older adults

BACKGROUND

Cognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters.

METHODS

We used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011-2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI).

RESULTS

Using the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36-12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62-7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race.

CONCLUSION

The model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.

New Picture Stimuli for the NIH Stroke Scale: A Validation Study

BACKGROUND

The National Institutes of Health Stroke Scale is a widely accepted tool for structured graded neurological examination of stroke or suspected stroke in the hyperacute setting. Concerns have arisen about the use of its picture stimuli in a contemporary and global health context. Here, we present new stimuli prepared to serve the needs of stroke providers worldwide: the precarious painter image description and updated objects for naming.

METHODS

This was a validation study of 101 healthy fluent English speakers. Participants were reached by the Johns Hopkins Outpatient Center, the University of South Carolina, and Prisma Health from 2022 to 2023 and included residents of the United States, Germany, Canada, the United Kingdom, Australia, and Zambia. Participants were recorded in person or via video conferencing when asked to describe the new picture, while a subset named seven illustrations. Multivariate analyses of variance were used for primary analyses. In a complementary investigation, 299 attendees of the 2023 International Stroke Conference were asked about their preference for the existing or new stimuli and why.

RESULTS

Each of the 44 content units from the picture description was included by at least 5% of respondents in the demographically representative subsample. Performance was similar across healthy participants irrespective of age, sex, race, ethnicity, or education. Typical descriptions were characterized by an average of 23 content units (SD=5) conveyed with 167 syllables (SD=79). The new naming stimuli were recognized by 100% of participants from many countries as being familiar and identifiable, and names provided in response to the task were highly convergent. The majority of stroke health care providers preferred both the precarious painter and naming stimuli.

CONCLUSIONS

The description of the new National Institutes of Health Stroke Scale picture, the precarious painter, results in rich samples among healthy speakers that will provide an appropriate basis for the detection of language deficits.

White matter hyperintensity load mediates the relationship between age and cognition

To elucidate the relationship between age and cognitive decline, it is important to consider structural brain changes such as white matter hyperintensities (WMHs), which are common in older age and may affect behavior. Therefore, we aimed to investigate if WMH load is a mediator of the relationship between age and cognitive decline. Healthy participants (N = 166, 20-80 years) completed the Montreal Cognitive Assessment (MoCA). WMHs were manually delineated on FLAIR scans. Mediation analysis was conducted to determine if WMH load mediates the relationship between age and cognition. Older age was associated with worse cognition (p < 0.001), but this was an indirect effect: older participants had more WMHs, and, in turn, increased WMH load was associated with worse MoCA scores. WMH load mediates the relationship between age and cognitive decline. Importantly, this relationship was not moderated by age (i.e., increased WMH severity is associated with poorer MoCA scores irrespective of age). Across all ages, high cholesterol was associated with increased WMH severity.

Lower socioeconomic status is associated with premature brain aging

BACKGROUND

Premature age-related brain changes may be influenced by physical health factors. Lower socioeconomic status (SES) is often associated with poorer physical health. In this study, we aimed to investigate the relationship between SES and premature brain aging.

METHODS

Brain age was estimated from T1-weighted images using BrainAgeR in 217 participants from the ABC@UofSC Repository. The difference between brain and chronological age (BrainGAP) was calculated. Multiple regression models were used to predict BrainGAP with age, SES, body mass index, diabetes, hypertension, sex, race, and education as predictors. SES was calculated from size-adjusted household income and the cost of living.

RESULTS

Fifty-five participants (25.35%) had greater brain age than chronological age (premature brain aging). Multiple regression models revealed that age, sex, and SES were significant predictors of BrainGAP with lower SES associated with greater BrainGAP (premature brain aging).

CONCLUSIONS

This study demonstrates that lower SES is an independent contributor to premature brain aging. This may provide additional insight into the mechanisms associated with brain health, cognition, and resilience to neurological injury.

Cerebral blood flow in patients recovered from mild COVID-19

BACKGROUND AND PURPOSE

Cerebral hypoperfusion has been described in both severe and mild forms of symptomatic Coronavirus Disease 2019 (COVID-19) infection. The purpose of this study was to investigate global and regional cerebral blood flow (CBF) in asymptomatic COVID-19 patients.

METHODS

Cases with mild COVID-19 infection and age-, sex-, and race-matched healthy controls were drawn from the Aging Brain Consortium at The University of South Carolina data repository. Demographics, risk factors, and data from the Montreal Cognitive Assessment were collected. Mean CBF values for gray matter (GM), white matter (WM), and the whole brain were calculated by averaging CBF values of standard space-normalized CBF image values falling within GM and WM masks. Whole brain region of interest-based analyses were used to create standardized CBF maps and explore differences between groups.

RESULTS

Twenty-eight cases with prior mild COVID-19 infection were compared with 28 controls. Whole-brain CBF (46.7 ± 5.6 vs. 49.3 ± 3.7, p = .05) and WM CBF (29.3 ± 2.6 vs. 31.0 ± 1.6, p = .03) were noted to be significantly lower in COVID-19 cases as compared to controls. Predictive models based on these data predicted COVID-19 group membership with a high degree of accuracy (85.2%, p < .001), suggesting CBF patterns are an imaging marker of mild COVID-19 infection.

CONCLUSION

In this study, lower WM CBF, as well as widespread regional CBF changes identified using quantitative MRI, was found in mild COVID-19 patients. Further studies are needed to determine the reliability of this newly identified COVID-19 brain imaging marker and determine what drives these CBF changes.

Abstract 109: Regional Cerebral Hypoperfusion In Patients Recovered From Mild COVID-19

Background: Cerebral hypoperfusion have been described in both severe and mild forms of symptomatic coronavirus disease 2019 (COVID-19) infection. The purpose of this study was to investigate global and regional gray matter (GM) and white matter (WM) cerebral blood flow (CBF) in asymptomatic COVID-19 infection patients compared with age-gender-race matched controls.

Methods: Cases with mild COVID-19 infection and age-gender-race matched healthy controls, were drawn from the ABC@UofSC data repository. Demographics, risk factors and data from the Montreal Cognitive Assessment (MOCA score) were collected within a week of magnetic resonance imaging (MRI) perfusion image acquisition using pseudo-continuous arterial spin labeling. Mean CBF values for GM, WM and whole brain were calculated by averaging CBF values of standard space normalized CBF image values falling within GM and WM masks. Whole-brain, region of interest (ROI) based analyses were used to create standardized cerebral blood flow maps and further explore differences between the two groups.

Results: Twenty-eight cases with prior mild COVID-19 infection were compared with 28 age-, gender-, race-matched controls. The MOCA score was similar between cases and controls. Whole-brain CBF (46.7±5.6 vs. 49.3±3.7, p=0.05), GM-specific CBF (64.2±8.9 vs. 67.6±6.0, p=0.10), and WM-specific CBF (29.3±2.6 vs. 31.0±1.6, p=0.03) were noted to be lower in COVID-19 cases as compared to controls. Further analysis identified several brain regions with lower CBF than the CONTROL group colors representing Z-scores shown in the figure. Predictive models based on these data predicted COVID-19 group membership with a high degree of accuracy (85.2%) suggesting CBF patterns as a key imaging marker of mild infection.

Conclusion: In this study, lower white matter CBF, as well as widespread regional CBF changes identified using quantitative MRI, were found in patients recovered from mild to moderate COVID-19 infection.

White matter hyperintensity load is associated with premature brain aging

BACKGROUND

Brain age is an MRI-derived estimate of brain tissue loss that has a similar pattern to aging-related atrophy. White matter hyperintensities (WMHs) are neuroimaging markers of small vessel disease and may represent subtle signs of brain compromise. We tested the hypothesis that WMHs are independently associated with premature brain age in an original aging cohort.

METHODS

Brain age was calculated using machine-learning on whole-brain tissue estimates from T1-weighted images using the BrainAgeR analysis pipeline in 166 healthy adult participants. WMHs were manually delineated on FLAIR images. WMH load was defined as the cumulative volume of WMHs. A positive difference between estimated brain age and chronological age (BrainGAP) was used as a measure of premature brain aging. Then, partial Pearson correlations between BrainGAP and volume of WMHs were calculated (accounting for chronological age).

RESULTS

Brain and chronological age were strongly correlated (r(163)=0.932, p<0.001). There was significant negative correlation between BrainGAP scores and chronological age (r(163)=-0.244, p<0.001) indicating that younger participants had higher BrainGAP (premature brain aging). Chronological age also showed a positive correlation with WMH load (r(163)=0.506, p<0.001) indicating older participants had increased WMH load. Controlling for chronological age, there was a statistically significant relationship between premature brain aging and WMHs load (r(163)=0.216, p=0.003). Each additional year in brain age beyond chronological age corresponded to an additional 1.1mm3 in WMH load.

CONCLUSIONS

WMHs are an independent factor associated with premature brain aging. This finding underscores the impact of white matter disease on global brain integrity and progressive age-like brain atrophy.

Low normal FMR1 genotype in older adult women: Psychological well-being and motor function

The FMR1 gene plays a key role in adult neurogenesis and neuroplasticity, and thus may contribute to age-related health in the population. The current study focused on the "low normal" FMR1 genotype, defined by lower-than-typical numbers of FMR1 CGG repeats (<26), as a potential genetic determinant of age-related health. We characterized the effect of the low normal FMR1 genotype on psychological well-being and motor function in a racially diverse non-clinical sample of older adult women. Women with low CGG repeats were distinguished from those with CGGs falling within the mid-high end of the normal range by reduced performance on multimodal assessments of motor function and psychological well-being, with large effect sizes. Robust continuous associations were also detected between lower CGG repeat length and reduced psychological well-being, balance, and dexterity. Findings suggest that FMR1 may represent an important mediator of individual differences in age-related health; larger epidemiological studies are needed. Given that approximately 23-35% of females carry the low normal genotype, efforts to understand its clinical effects have relevance a broad swath of the aging population.

Training as an Intervention to Decrease Medical Record Abstraction Errors Multicenter Studies

Studies often rely on medical record abstraction as a major source of data. However, data quality from medical record abstraction has long been questioned. Electronic Health Records (EHRs) potentially add variability to the abstraction process due to the complexity of navigating and locating study data within these systems. We report training for and initial quality assessment of medical record abstraction for a clinical study conducted by the IDeA States Pediatric Clinical Trials Network (ISPCTN) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Neonatal Research Network (NRN) using medical record abstraction as the primary data source. As part of overall quality assurance, study-specific training for medical record abstractors was developed and deployed during study start-up. The training consisted of a didactic session with an example case abstraction and an independent abstraction of two standardized cases. Sixty-nine site abstractors from thirty sites were trained. The training was designed to achieve an error rate for each abstractor of no greater than 4.93% with a mean of 2.53%, at study initiation. Twenty-three percent of the trainees exceeded the acceptance limit on one or both of the training test cases, supporting the need for such training. We describe lessons learned in the design and operationalization of the study-specific, medical record abstraction training program.