Citations

The immune system substantially influences age-related cognitive decline and Alzheimer’s disease (AD) progression, affected by genetic and environmental factors. In a Mayo Clinic Study of Aging cohort, we examined how risk factors like APOE genotype, age, and sex affect inflammatory molecules and AD biomarkers in cerebrospinal fluid (CSF). Among cognitively unimpaired individuals over 65 ( N = 298), we measured 365 CSF inflammatory molecules, finding age, sex, and diabetes status predominantly influencing their levels. We observed age-related correlations with AD biomarkers such as total tau, phosphorylated tau-181, neurofilament light chain (NfL), and YKL40. APOE4 was associated with lower Aβ42 and higher SNAP25 in CSF. We explored baseline variables predicting cognitive decline risk, finding age, CSF Aβ42, NfL, and REG4 to be independently correlated. Subjects with older age, lower Aβ42, higher NfL, and higher REG4 at baseline had increased cognitive impairment risk during follow-up. This suggests that assessing CSF inflammatory molecules and AD biomarkers could predict cognitive impairment risk in the elderly.

Background: Little is known about the association between physical activity (PA) and cognitive trajectories in older adults. Objective: To examine the association between PA and change in memory, language, attention, visuospatial skills, and global cognition, and a potential impact of sex or Apolipoprotein E (APOE) ε4 status. Methods: Longitudinal study derived from the population-based Mayo Clinic Study of Aging, including 2,060 cognitively unimpaired males and females aged ≥70 years. Engagement in midlife (ages 50–65) and late-life (last year) PA was assessed using a questionnaire. Neuropsychological testing was done every 15 months (mean follow-up 5.8 years). We ran linear mixed-effect models to examine whether mid- or late-life PA at three intensities (mild, moderate, vigorous) was associated with cognitive z-scores. Results: Light intensity midlife PA was associated with less decline in memory function compared to the no-PA reference group (time x light PA; estimate [standard error] 0.047 [0.016], p = 0.004). Vigorous late-life PA was associated with less decline in language (0.033 [0.015], p = 0.030), attention (0.032 [0.017], p = 0.050), and global cognition (0.039 [0.016], p = 0.012). Females who were physically inactive in midlife experienced more pronounced cognitive decline than females physically active in midlife and males regardless of PA (p-values for time interaction terms with midlife PA levels and sex were all p < 0.05 for global cognition). APOE ε4 carriership did not moderate the association between PA and cognition. Conclusion: Engaging in PA, particularly of vigorous intensity in late-life, was associated with less pronounced decline in global and domain-specific cognition. This association may differ by sex.

With increasing number of people living with dementia, the problem of late diagnosis significantly impacts a person’s quality of life while early signs of dementia may provide useful insights to facilitate better treatment plans. With time, this progressive neurodegenerative syndrome could progress from mild cognitive impairment to dementia. A pattern of health conditions can be characterized in unsupervised manner to help predict this progress. As a significant extension to our previous work with streaming clustering model, we consider additional information for predicting dementia onset. With empirical observations, we discover the importance of examining sex and age to predict dementia onset. To this end, we propose a sex-specific model with age-constraint for predicting dementia onset and validate the effectiveness of our models using data from Mayo Clinic Study of Aging (MCSA). The proposed sex-specific models for older adult populations (>=65 years of age) outperformed the previous models with F-score of 77% and 78% for male-specific and female-specific models, respectively. Our experiments of sex-specific temporal clustering of features in older adults demonstrate the potential of more personalized models for early alerts of dementia.

AbstractBackgroundThe mapping between risk factors and biomarkers with cognitive performance and decline in the older adults is complex and varies significantly across different age and sex groups. Our goals were to (i) develop a machine learning (ML) model to predict baseline cognition and cognitive decline over a 5‐year follow‐up using only baseline assessments, and (ii) identify important modifiable and non‐modifiable predictors of cognitive decline across different age strata.MethodWe utilized longitudinal data from 2219 participants (mean age (SD): 70 (12) years, 49% male) from Mayo Clinic Study of Aging who had 43 features belonging to cardiovascular health, plasma, MRI (diffusion and structural), and lifestyle measures. Cognition was measured using a global cognition z‐score. We built ML models to predict baseline cognition and rate of cognitive decline over 5‐years (linear regression slopes over all individual followup measurements for each participant) using baseline assessments as inputs. We then built a model for each category separately and as a pooled model with all measures included, incorporating education and sex as demographic features in all models (Fig. 1). We applied a multi‐layer stack ensemble ML technique that utilized repeated k‐fold bagging to maximize accuracy. Importance of the top predictors were assessed in three age strata (55‐65 years (31%), 65‐80 years (46%), 80‐95 years (23%)).ResultThe pooled model explained the largest variance for prediction of baseline cognition and cognitive decline (Fig. 2). In individuals ≤80 years, lower NfL was most predictive of higher baseline cognition. A combination of imaging and plasma features predicted the 5‐year cognitive decline. Additionally, in individuals ≤65 years, diabetes and hypertension were important predictors of faster cognitive decline and in those >80, lower BMI and midlife physical/cognitive activity was an important predictor of faster cognitive decline (Fig. 3).ConclusionThe plasma model was best for baseline cognition prediction and imaging model was best for cognitive decline prediction. There was significant heterogeneity in important features across the age strata. In individuals >65 years, plasma GFAP was the most important predictor of cognitive decline. Our model allowed identification of key risk factors that predicted cognitive decline.

AbstractBackgroundMost studies on the relationship between neuropsychiatric symptoms (NPS) and cognitive decline focus on NPS data collected over a short period of time. To better understand the association between NPS and cognitive decline, including their temporal relationship, it is important to know NPS status across a person’s life.MethodWe utilized the unique Rochester Epidemiology Project medical record linkage system that has captured medical records as far back as 1907, to abstract the psychiatric history of 1810 cognitively unimpaired participants in the population‐based Mayo Clinic Study of Aging (MCSA). Trained nurse abstractors ascertained the presence of exposure to three common NPS (anxiety, depression, and unspecified emotional distress [UED]), both before and after entry into the MCSA. We conducted Cox Proportional Hazards (PH) models using age as the time scale and adjusting for sex, education, and APOE E4 allele for each NPS in predicting mild cognitive impairment (MCI), or dementia. Additional analyses considering medication use (yes/no) and timing of NPS were also run.ResultAnxiety (Hazard Ratio [HR] = 1.47, p<0.001), depression (HR = 1.36, p = 0.001), and UED (HR = 1.22, p = 0.039) were associated with a higher risk of MCI. These associations were present regardless of medication use for anxiety (With medication: HR = 1.51, p<0.001; without medication: HR = 1.41, p = 0.015), and for those taking medications for depression (With medication: HR = 1.47, p<0.001; without medication: 1.09, p = 0.597). Anxiety was associated with MCI regardless of timing of onset with the strongest relationship being for those with onset of anxiety before 65 that persisted into later life (HR = 1.88, p = 0.004). Depression was associated regardless of timing of onset if the depression was present in late life too. The results were similar for the outcome of dementia (Anxiety: HR = 1.62, p<0.001; depression: HR = 1.31, p = 0.040; UED: HR = 1.37, p = 0.014). The associations accounting for medication use and timing of NPS were similar for the outcome of dementia too.ConclusionNPS is associated with progression to MCI and dementia. Both medication use and timing of NPS might play a role in these associations.

AbstractBackgroundSmall vessel disease (SVD) is a common vascular contributor to cognitive impairment and dementia. In this work, we evaluated and compared non‐imaging measures that can predict variability in SVD related white matter (WM) damage in a population‐based sample.MethodWe identified 752 participants in the population‐based sample of Mayo Clinic Study of Aging (≥50 years of age), who had at least 2 diffusion tensor imaging (DTI) scans and white matter hyperintensity (WMH) assessments along with baseline demographics, systemic vascular health, lifestyle, motor impairment, behavioral symptoms, and plasma markers (Summarized in Figure 1). We computed the WM markers of SVD: fractional anisotropy of genu of the corpus callosum (Genu‐FA) representative of early WM changes, WMH representative of late WM changes, and Vascular WM score (combination of Genu‐FA and WMH). Using all available data, we fit linear mixed effect models and evaluated predictors of WM markers by considering each category of features independently (with demographics) as well as by combining all categories of featuresResultPlasma, gait, systemic vascular, and life‐style measures were significant predictors of all three WM markers of SVD (Figure 2). The predictors of early and late WM slightly differed such that there were no additional significant contributions from behavioral and plasma measures to Genu‐FA. In the final composite models shown in Table 1, higher gait speed, lower number of cardiovascular and metabolic conditions (CMC) and lower Unified Parkinson’s disease rating scale scores were associated with better Genu‐FA whereas higher CMC, Beck Anxiety Inventory scores (BAI), and glial acidic protein (GFAP) levels were associated with higher burden of WMH.ConclusionOur observations evaluated the non‐imaging markers of greater WM damage that are relevant to vascular contributions to cognitive impairment to dementia (VCID). We found that in addition to higher vascular risk, worse plasma GFAP, gait measures, and BAI scores were predictors of greater WM damage. Some non‐imaging markers were more associated with early WM changes and others with late WM changes.