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Evidence suggests exposure to particulate matter with aerodynamic diameter

IntroductionIn a geographically diverse sample of women, we asked whether cognitive reserve (CR) is best viewed as a general or cognitive domain-specific construct and whether some cognitive reserve domains but not others exert protective effects on risk of developing mild cognitive impairment (MCI) or dementia.MethodsEstimates of general and domain-specific CR were derived via variance decomposition in 972 cognitively intact women from the Women's Health Initiative Study of Cognitive Aging and Women's Health Memory Study Magnetic Resonance Imaging. Women were then followed up for 13 years.ResultsGeneral CR was the strongest predictor of reduced risk for both MCI and dementia, compared to domain-specific CR measures. Verbal memory, figural memory, and spatial CR were independently protective of MCI, but only verbal memory was independently associated with reduced risk for dementia.DiscussionCognitive reserve is a heterogenous construct with valid quantitative measures identifiable across different neuropsychological processes associated with MCI and dementia.

Background. Existing self-report measures of mobility ignore important contextual features of movement and require respondents to make complex judgments about specific tasks. Thus, we describe the development and validation of a short form (sf) video-animated tool for assessing mobility, the Mobility Assessment Tool--MAT-sf. Methods. This study involves cross-sectional and longitudinal analyses examining the measurement properties of the MAT-sf. The MAT-sf consists of 10 animated video clips that assess respondents' level of proficiency in performing each task. The main outcome measures used for validation included the Pepper Assessment Tool for Disability (PAT-D), the Short Physical Performance Battery (SPPB), and 400-m walk test. Results. Participants (n = 234), 166 women and 68 men, had an average age of 81.9 years and a variety of comorbidities with 65.4% having high blood pressure. An average SPPB score of 8.6 (range 2-12) suggests that the study sample had evidence of compromised physical function but was quite heterogeneous. The MAT-sf had good content validity, excellent tes--retest reliability (r = .93), and criterion-related validity with the PAT-D. Moreover, the MAT-sf added considerable variance to the prediction of both SPPB scores and 400-m gait speed over and above the PAT-D mobility subscale. The MAT-sf also discriminated between older adults who completed or failed the 400-m walk test. Conclusion. The MAT-sf is an innovative psychometrically sound measure of mobility. It has utility in epidemiological studies, translational science, and clinical practice. Key Words: Mobility--Aging--Measurement--Disability. doi: 10.1093/gerona/glq055

Introduction: A data‐driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD‐PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. Methods: AD‐PS scores were generated for 839 cognitively non‐impaired individuals with a mean follow‐up of 4.86 years. The scores and a hypothesis‐driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. Results: Logistic regression analyses suggest the data‐driven AD‐PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) ε4 carriers. Random forest analyses including predictors from different domains ranked the AD‐PS scores as the most relevant MRI predictor of cognitive impairment. Conclusions: Overall, the AD‐PS scores were the stronger MRI‐derived predictors of incident cognitive impairment in cognitively non‐impaired individuals. [ABSTRACT FROM AUTHOR]

Background: Muscle metrics derived from computed tomography (CT) are associated with adverse health events in older persons, but obtaining these metrics using current methods is not practical for large datasets. We developed a fully automated method for muscle measurement on CT images. This study aimed to determine the relationship between muscle measurements on CT with survival in a large multicenter trial of older adults.Method: The relationship between baseline paraspinous skeletal muscle area (SMA) and skeletal muscle density (SMD) and survival over 6 years was determined in 6,803 men and 4,558 women (baseline age: 60-69 years) in the National Lung Screening Trial (NLST). The automated machine learning pipeline selected appropriate CT series, chose a single image at T12, and segmented left paraspinous muscle, recording cross-sectional area and density. Associations between SMA and SMD with all-cause mortality were determined using sex-stratified Cox proportional hazards models, adjusted for age, race, height, weight, pack-years of smoking, and presence of diabetes, chronic lung disease, cardiovascular disease, and cancer at enrollment.Results: After a mean 6.44 ± 1.06 years of follow-up, 635 (9.33%) men and 265 (5.81%) women died. In men, higher SMA and SMD were associated with a lower risk of all-cause mortality, in fully adjusted models. A one-unit standard deviation increase was associated with a hazard ratio (HR) = 0.85 (95% confidence interval [CI] = 0.79, 0.91; p < .001) for SMA and HR = 0.91 (95% CI = 0.84, 0.98; p = .012) for SMD. In women, the associations did not reach significance.Conclusion: Higher paraspinous SMA and SMD, automatically derived from CT exams, were associated with better survival in a large multicenter cohort of community-dwelling older men. [ABSTRACT FROM AUTHOR]

Background: MRI is a widely used modality to evaluate dementia risk. Here, we extend previous work by estimating the Alzheimer's disease pattern similarity (AD‐PS) scores for participants of a community‐dwelling older adult cohort to evaluate neuroanatomic risk of AD using MRI. Method: Baseline MRI data were available from 522 participants from the Wake Forest Alzheimer's Disease Research Center's (ADRC) Clinical Core with mean age 70.2, 81.2% White and 17.6% African American. Of those, 253 were cognitively impaired (dementia and mild cognitive impairment (MCI)) and 138 underwent [11C]PiB PET (MCI = 48 and dementia = 19). Global PiB SUVR was averaged from a cortical region of interest sensitive to early AD. Participants were classified as amyloid positivity using a previously defined threshold (≥1.21 SUVR). AD‐PS scores were estimated based on GM probability maps according to previously reported machine learning methodology[1]. High‐dimensional classifiers were trained using ADNI MRI data. To generate the AD‐PS scores, MRI data from the ADRC cohort was provided as input to the classifiers. We investigated associations that AD‐PS scores [range 0‐1] had with amyloid positivity and cognitive impairment. All analyses were adjusted for age, sex, race and education. AD‐PS scores discrimination of participants with dementia versus normal cognition (CN) was compared to hippocampal volume and cortical thickness in temporal regions based meta‐ROI which are sensitive to AD. Result: AD‐PS scores were strongly associated with cognitive impairment: OR = 4.1, CI: 95% [2.9‐5.9], p<0.0001. In addition, they were associated with amyloid positivity with OR = 2.8,CI: 95% [1.7‐4.7], p<0.001, N = 138. These amyloid positivity results did not hold in analyses limited to CN participants only(N = 71). Performance when discriminating patients with dementia from CN individuals was highest for AD‐PS score (AUC = 92.2, CI: 95% [86.9‐97.5]), followed by hippocampal volume (AUC = 86.5, CI: 95% [81.1‐91.2]) and cortical thickness in temporal regions based meta‐ROI (AUC = 81.5 CI: 95% [74.4‐88.6], See‐Figure). Conclusion: This work highlights further the generalization performance of our machine learning algorithm. AD‐PS scores, a measure of dementia neuroanatomic risk, strongly discriminated individuals with dementia from CN individuals in the Wake Forest ADRC. References [1] Casanova et. al., Alzheimer's & Dementia, 2021, PMID:34310039. [ABSTRACT FROM AUTHOR]

Background: Machine learning and artificial intelligence methods have been applied to brain images to estimate measures of brain aging. However, only rarely have these measures been examined in the context of biologic age. Here, we investigated associations of an MRI‐based measure of dementia risk the Alzheimer's disease pattern similarity (AD‐PS) scores with measures of longevity and biological age. Method: Participants were those from visit 5 of the Atherosclerosis Risk in Community Study with cognitive status adjudication and MRI available. The AD‐PS score was estimated based on previously reported machine learning methods. We evaluated associations of the AD‐PS score with all‐cause mortality. Participants were stratified by AD‐PS tertiles and the analyses were adjusted for age, race, sex, hypertension and smoking. AD‐PS score was examined in association with 32 proteins reported to be associated with age. In these analyses, we used Bonferroni correction (α = 0.05,p<0.0016). Finally, associations with a deficit accumulation index(DAI) based on 38 health items was investigated. In both cases, linear regression models were adjusted for age, race and sex. Sensitivity analyses using only cognitively normal (CN) individuals were performed. Result: Mortality – A total of 356 participants died within 8 years of follow‐up. The AD‐PS score was significantly associated(p<0.001) with time to all‐cause mortality. Participants in the lowest tertile had lower all‐cause mortality rate compared to those in the highest tertile (HR: 0.43;95% CI:0.31,0.60). The association remained significant when restricting the sample to only CN subjects (HR for lowest tertile:0.53; 95% CI:0.35,0.81, p = 0.0028). Age related proteins ‐ The AD‐PS scores were significantly associated (p<0.05, uncorrected) with 10 of the 32 proteins. Growth/differentiation factor 15(GDF‐15) and pleiotrophin remained significant after correction for multiple‐testing. Analysis of CN participants showed a subset of the same proteins to be significant (p<0.05,uncorrected) but only the GDF‐15 remained significant after correction for multiple‐testing. DAI ‐ A linear regression model showed a significant association between DAI and AD‐PS scores overall (coeff = 0.52; 95% CI:0.37‐0.67) and in the CN subset (coeff = 0.3,95% CI:0.13‐0.48). Conclusion: While the AD‐PS scores were created as a measure of dementia risk, our analyses suggest that they could also be capturing brain aging. [ABSTRACT FROM AUTHOR]