Associations of Brain Pathology Cognitive and Physical Markers With Age in Cognitively Normal Individuals Aged 60-102 Years

Cerebrospinal fluid proteomics in patients with Alzheimer's disease reveals five molecular subtypes with distinct genetic risk profiles

Alzheimer's disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood-brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine.

Exploratory and confirmatory factor analyses identify three structural dimensions for measuring physical function in community-dwelling older adults

Background

Physical function is a strong indicator of biological age and quality of life among older adults. However, the results from studies exploring the structural dimensions of physical function are inconsistent, and the measures assessed vary greatly, leading to a lack of comparability among them. This study aimed to construct a model to identify structural dimensions that are suitable and best assess physical function among community-dwelling adults 60-74 years of age in China.

Method

This study was conducted in 11 communities in Shanghai, China, from May to July 2021. A total of 381 adults 60-74 years of age were included in the study. Measured physical function data were used in factor analyses. Data collected from individuals were randomly assigned to either exploratory factor analysis (EFA) (n = 190) or confirmatory factor analysis (CFA) (n = 191). The statistical software used in the study was SPSS for EFA and AMOS for CFA. To test the properties of the structural dimension model of physical function, various fit indices, convergent validity, and discriminant validity were calculated.

Results

The EFA results derived seven indicators in three factors, with 58.548% of the total variance explained. The three factors were mobility function (three indicators), which explained 26.380% of the variance, handgrip strength and pulmonary function (two indicators), which explained 19.117% of the variance, and muscle strength (two indicators) which explained 13.050% of the variance. The CFA indicated that this model had an acceptable fit (χ2/df ratio, 2.102; GFI, 0.967; IFI, 0.960; CFI, 0.959; and RMSEA, 0.076), and the criteria for convergent validity and discriminability were also met by the model.

Conclusion

The constructed structural dimension model of physical function appeared to be a suitable and reliable tool to measure physical function in community-dwelling adults aged 60-74 years in China. The structural dimension indicators identified by this model may help sports medicine experts and healthcare providers offer more targeted interventions for older adults to reverse or slow the decline of physical function and to offer actionable targets for healthy aging in this population.

Trajectories of Mini-Mental State Examination Scores over the Lifespan in General Populations: A Systematic Review and Meta-Regression Analysis

Objectives: Over the lifespan cumulative changes to the brain lead to cognitive decline and eventually to dementia in 20-25% of adults 85 years and older. A commonly used screening tool for cognitive function is the Standard 30 point Mini-Mental State Examination (MMSE). Though the MMSE is used to screen for dementia, little is known about the changes in scores over the lifespan in general populations.Method: A systematic search was conducted using Cochrane, EMBASE, MEDLINE and PsycINFO for articles published from January 1, 2007 to May 25, 2017. Articles were included if they had a longitudinal design reporting at least two MMSE scores. A mixed-effect meta-regression analysis was conducted to examine the influence of age on MMSE score followed by a change-point regression analysis determining the age at which MMSE declines.Results: 45 articles including 58,939 individuals (age range 18-108 years, 61.2% female) summarized 222 MMSE point estimates from 35 cohorts. The meta-regression demonstrated a significant decrease in MMSE scores with higher age (regression coefficient of age: -0.10 (Confidence Interval (CI) -0.15, -0.05)). The average annual decline in MMSE scores identified by the change-point analysis at the age of 41 years and 84 years were -0.04 (95% CI: -0.05, -0.03) and -0.53 (95% CI: -0.55, -0.50), respectively.Conclusions: Between the age of 29 and 105 years MMSE scores decline, with the highest decline between age 84 and 105 years.Clinical Implementations: The use of MMSE should be restricted to higher age categories in aging general populations.

The Association Between Functional Assessment and Structural Brain Biomarkers in an Ethnically Diverse Sample With Normal Cognition, Mild Cognitive Impairment, or Dementia

OBJECTIVE

To investigate the association between the functional activities questionnaire (FAQ) and brain biomarkers (bilateral hippocampal volume [HV], bilateral entorhinal volume [ERV], and entorhinal cortical thickness [ERT]) in cognitively normal (CN) individuals, mild cognitive impairment (MCI), or dementia.

METHOD

In total, 226 participants (137 females; mean age = 71.76, SD = 7.93; Hispanic Americans = 137; European Americans = 89) were assessed with a comprehensive clinical examination, a neuropsychological battery, a structural magnetic resonance imaging, and were classified as CN or diagnosed with MCI or dementia. Linear regression analyses examined the association between functional activities as measured by the FAQ on brain biomarkers, including HV, ERV, and ERT, controlling for age, education, global cognition, gender, and ethnicity.

RESULTS

The FAQ significantly predicted HV, ERV, and ERT for the entire sample. However, this association was not significant for ERV and ERT when excluding the dementia group. The FAQ score remained a significant predictor of HV for the non-dementia group. Age, education, gender, ethnicity, Montreal Cognitive Assessment score, and FAQ were also significant predictors of HV for the overall sample, suggesting that younger Hispanic females with fewer years of education, higher global mental status, and better functioning, were more likely to have larger HV.

CONCLUSION

FAQ scores were related to HV in older adults across clinical groups (CN, MCI, and dementia), but its association with the entorhinal cortex was driven by individuals with dementia. Demographic variables, including ethnicity, additionally influenced these associations.

Amyloid-β, cortical thickness, and subsequent cognitive decline in cognitively normal oldest-old

Objective

To investigate the relationship between amyloid‐β (Aβ) deposition and markers of brain structure on cognitive decline in oldest‐old individuals with initial normal cognition.

Methods

We studied cognitive functioning in four domains at baseline and change over time in fifty‐seven cognitively intact individuals from the EMIF‐AD 90+ study. Predictors were Aβ status determined by [¹⁸F]‐flutemetamol PET (normal = Aβ − vs. abnormal = Aβ+), cortical thickness in 34 regions and hippocampal volume. Mediation analyses were performed to test whether effects of Aβ on cognitive decline were mediated by atrophy of specific anatomical brain areas.

Results

Subjects had a mean age of 92.7 ± 2.9 years, of whom 19 (33%) were Aβ+. Compared to Aβ−, Aβ+ individuals showed steeper decline on memory (β ± SE = −0.26 ± 0.09), and processing speed (β ± SE = −0.18 ± 0.08) performance over 1.5 years (P < 0.05). Furthermore, medial and lateral temporal lobe atrophy was associated with steeper decline in memory and language across individuals. Mediation analyses revealed that part of the memory decline observed in Aβ+ individuals was mediated through parahippocampal atrophy.

Interpretation

These results show that Aβ abnormality even in the oldest old with initially normal cognition is not part of normal aging, but is associated with a decline in cognitive functioning. Other pathologies may also contribute to decline in the oldest old as cortical thickness predicted cognitive decline similarly in individuals with and without Aβ pathology.