Cross-sectional association between posttraumatic stress and cognition is moderated by pulmonary functioning in world trade center responders.

BACKGROUND: Posttraumatic stress disorder (PTSD) symptomatology and poorer pulmonary function are highly prevalent psychiatric and medical conditions. In the present study, we tested for the individual, additive, and modifying associations of PTSD symptomatology and pulmonary function with cognitive performance. METHODS: In this cross-sectional study, a total of 1,401 World Trade Center (WTC) responders (mean age = 53, SD = 8 years, 92% males) participated in the study. Cogstate assessment measured cognitive performance. PTSD symptomatology was measured using the trauma-specific version of the posttraumatic stress disorder checklist (PCL-17) adapted for the WTC attacks. The 1-second forced expiratory volume and forced vital capacity (FEV1/FVC) ratio was used to measure pulmonary function. Linear regressions with cognitive performance as the outcome were conducted to assess individual, additive, and moderating associations of PTSD symptomatology and pulmonary function. RESULTS: Higher PTSD symptomatology and poorer pulmonary function were negatively associated with cognitive performance. A 10% increase on the FEV1/FVC ratio moderated the association between PTSD symptomatology and cognition, whereby its association with cognition was stronger when PTSD symptomatology was higher (est. = 0.01, 95%CI = 0.004, 0.01, p < 0.001). When stratified by responder type, these associations persisted in trained (est. = 0.01, 95%CI = 0.01, 0.02, p < 0.001), but not in non-trained (est. = 0.004, 95% C.I. = -0.01, 0.02, p = 0.39) responders. CONCLUSIONS: In the presence of higher PTSD, better pulmonary functioning is associated with better cognitive performance. Early intervention efforts to mitigate preventable cognitive decline in high-risk populations should be studied, especially since intervention in one modality may have an impact on others.

Inferring Alzheimer's Disease Pathologic Traits from Clinical Measures in Living Adults.

Background: Alzheimer's disease neuropathologic changes (AD-NC) are important to identify people with high risk for AD dementia (ADD) and subtyping ADD. Objective: Develop imputation models based on clinical measures to infer AD-NC. Methods: We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-ß, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD. Results: Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2 = (0.188,0.316,0.262) respectively for amyloid-ß, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC = (0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values < 1.4×10-10. Conclusions: Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.

Glycoproteome-Wide Discovery of Cortical Glycoproteins That May Provide Cognitive Resilience in Older Adults.

BACKGROUND AND OBJECTIVES: Molecular omics studies have identified proteins related to cognitive resilience but unrelated to Alzheimer disease and Alzheimer disease-related dementia (AD/ADRD) pathologies. Posttranslational modifications of proteins with glycans can modify protein function. In this study, we identified glycopeptiforms associated with cognitive resilience. METHODS: We studied brains from adults with annual cognitive testing with postmortem indices of 10 AD/ADRD pathologies and proteome-wide data from dorsal lateral prefrontal cortex (DLPFC). We quantified 11, 012 glycopeptiforms from DLPFC using liquid chromatography with tandem mass spectrometry. We used linear mixed-effects models to identify glycopeptiforms associated with cognitive decline correcting for multiple comparisons (p < 5 × 10-6). Then, we regressed out the effect of AD/ADRD pathologies to identify glycopeptiforms that may provide cognitive resilience. RESULTS: We studied 366 brains, average age at death 89 years, and 70% female with no cognitive impairment = 152, mild cognitive impairment = 93, and AD = 121 cognitive status at death. In models adjusting for age, sex and education, 11 glycopeptiforms were associated with cognitive decline. In further modeling, 8 of these glycopeptiforms remained associated with cognitive decline after adjusting for AD/ADRD pathologies: NPTX2a (Est., 0.030, SE, 0.005, p = 1 × 10-4); NPTX2b (Est.,0.019, SE, 0.005, p = 2 × 10-4) NECTIN1(Est., 0.029, SE, 0.009, p = 9 × 10-4), NPTX2c (Est., 0.015, SE, 0.004, p = 9 × 10-4), HSPB1 (Est., -0.021, SE, 0.006, p = 2 × 10-4), PLTP (Est., -0.027, SE, 0.009, p = 4.2 × 10-3), NAGK (Est., -0.027, SE, 0.008, p = 1.4 × 10-3), and VAT1 (Est., -0.020, SE, 0.006, p = 1.1 × 10-3). Higher levels of 4 resilience glycopeptiforms derived through glycosylation were associated with slower decline and higher levels of 4 derived through glycation were related to faster decline. Together, these 8 glycopeptiforms accounted for an additional 6% of cognitive decline over the 33% accounted for the 10 brain pathologies and demographics. All 8 resilience glycopeptiforms remained associated with cognitive decline after adjustments for the expression level of their corresponding protein. Exploratory gene ontology suggested that molecular mechanisms of glycopeptiforms associated with cognitive decline may involve metabolic pathways including pyruvate and NADH pathways and highlighted the importance of molecular mechanisms involved in glucose metabolism. DISCUSSION: Glycopeptiforms in aging brains may provide cognitive resilience. Targeting these glycopeptiforms may lead to therapies that maintain cognition through resilience.

Proteome-wide Analyses Identified Cortical Proteins Associated With Resilience for Varied Cognitive Abilities.

BACKGROUND AND OBJECTIVES: Prior work suggests that cognitive resilience may contribute to the heterogeneity of cognitive decline. This study examined whether distinct cortical proteins provide resilience for different cognitive abilities. METHODS: Participants were from the Religious Orders Study or the Rush Memory and Aging Project who had undergone annual assessments of 5 cognitive abilities and postmortem assessment of 9 Alzheimer disease and related dementia (ADRD) pathologies. Proteome-wide examination of the dorsolateral prefrontal cortex using tandem mass tag and liquid chromatography-mass spectrometry yielded 8,425 high-abundance proteins. We applied linear mixed-effect models to quantify residual cognitive change (cognitive resilience) of 5 cognitive abilities by regressing out cognitive decline related to age, sex, education, and indices of ADRD pathologies. Then we added terms for each of the individual proteins to identify cognitive resilience proteins associated with the different cognitive abilities. RESULTS: We included 604 decedents (69% female; mean age at death = 89 years) with proteomic data. A total of 47 cortical proteins that provide cognitive resilience were identified: 22 were associated with specific cognitive abilities, and 25 were common to at least 2 cognitive abilities. NRN1 was the only protein that was associated with more than 2 cognitive abilities (semantic memory: estimate = 0.020, SE = 0.004, p = 2.2 × 10-6; episodic memory: estimate = 0.029, SE = 0.004, p = 5.8 × 10-1; and working memory: estimate = 0.021, SE = 0.004, p = 1.2 × 10-7). Exploratory gene ontology analysis suggested that among top molecular pathways, mitochondrial translation was a molecular mechanism providing resilience in episodic memory, while nuclear-transcribed messenger RNA catabolic processes provided resilience in working memory. DISCUSSION: This study identified cortical proteins associated with various cognitive abilities. Differential associations across abilities may reflect distinct underlying biological pathways. These data provide potential high-value targets for further mechanistic and drug discovery studies to develop targeted treatments to prevent loss of cognition.

Inferring Alzheimer's disease pathologic traits from clinical measures in living adults.

Background: Alzheimer's disease neuropathologic changes (AD-NC) are important for identify people with high risk for AD dementia (ADD) and subtyping ADD. Objective: Develop imputation models based on clinical measures to infer AD-NC. Methods: We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-ß, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD. Results: Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2=(0.188,0.316,0.262) respectively for amyloid-ß, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC=(0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values<1.4 × 10-10. Conclusion: Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.

A prospective study of the association of cognitive and motor function with odds of life space constriction in older adults.

BACKGROUND: Many studies indicate that smaller life space is related to worse cognitive and motor function. It is plausible that cognitive and motor function also predict life space constriction, thus long-term, prospective studies are needed of cognitive and motor function as predictors of life space. METHODS: A total of 1246 participants of the Rush Memory and Aging Project, who reported initial maximal life space and at least one follow-up assessment were included in this prospective study, with up to 19 years follow-up. The outcome of interest was the Modified version of the Life Space Questionnaire; which we categorized into large (beyond community), medium (neighborhood/community), and small (home/yard) life space. Participants also had detailed composite measures of global cognition and motor function as predictors and available at the first life space assessment. Life space transitions over one-year periods were modeled using multistate Markov modeling, including confounders and both predictors simultaneously. RESULTS: Better cognitive and motor function were broadly associated with lower odds of life space constriction (Cognitive: Large ➔ medium: OR = 0.91, 95% CI 0.83-1.00; Large ➔ small: OR = 0.85, 95% CI 0.74-0.97; Medium ➔ small: OR = 1.01, 95% CI 0.82-1.22. Motor: large ➔ medium: OR = 0.76, 95% CI 0.69-0.83; large ➔ small: OR = 0.58, 95% CI 0.51-0.67; medium ➔ small: OR = 0.71, 95% CI = 0.57-0.87). CONCLUSIONS: Combined with previous literature that life space predicts function, these results support the notion of complex inter-relations of cognitive function, motor function, and life space.

Alzheimer's Disease Pathology Outside of the Cerebrum Is Related to a Higher Odds of Dementia.

BACKGROUND: Assessments of Alzheimer's disease pathology do not routinely include lower brainstem, olfactory bulb, and spinal cord. OBJECTIVE: Test if amyloid-ß (Aß) and paired helical filament (PHF) tau-tangles outside the cerebrum are associated with the odds of dementia. METHODS: Autopsies were obtained in decedents with cognitive testing (n = 300). Aß plaques and PHF tau-tangles were assessed in 24 sites: cerebrum (n = 14), brainstem (n = 5), olfactory bulb, and four spinal cord levels. Since spinal Aß were absent in the first 165 cases, it was not assessed in the remaining cases. RESULTS: Age at death was 91 years old. About 90% had Aß in cerebrum and of these, half had Aß in the brainstem. Of the latter, 85% showed Aß in the olfactory bulb. All but one participant had tau-tangles in the cerebrum and 86% had brainstem tau-tangles. Of the latter, 80% had tau-tangles in olfactory bulb and 36% tau-tangles in one or more spinal cord levels. About 90% of adults with tau-tangles also had Aß in one or more regions. In a logistic model controlling for demographics, Aß and tau-tangles within the cerebrum, the presence of Aß in olfactory bulb [OR, 1.74(1.00, 3.05)]; tau-tangles in brainstem [OR, 4.00(1.1.57,10.21)]; and spinal cord [OR, 1.87 (1.21,3.11)] were independently associated with higher odds of dementia. CONCLUSION: Regional differences in Aß and tau-tangle accumulation extend beyond cerebrum to spinal cord and their presence outside the cerebrum are associated with a higher odds of dementia. Further studies are needed to clarify the extent, burden, and consequences of AD pathology outside of cerebrum.

Correlated decline of cognitive and motor phenotypes and ADRD pathologies in old age.

INTRODUCTION: Examining motor and cognitive decline in separate models may underestimate their associations. METHODS: In a single trivariate model, we examined the levels and rates of decline of three phenotypes, sensor-derived total daily physical activity, motor abilities, and cognition in 1007 older adults during 6 years of follow-up. In 477 decedents, we repeated the model adding fixed terms for indices of nine brain pathologies. RESULTS: Simultaneous rates of decline of all three phenotypes showed the strongest correlations with shared variance of up to 50%. Brain pathologies explained about 3% of the variance of declining daily physical activity, 9% of declining motor abilities, and 42% of cognitive decline. DISCUSSION: The rates of declining cognitive and motor phenotypes are strongly correlated and measures of brain pathologies account for only a small minority of their decline. Further work is needed to elucidate the biology underlying correlated cognitive and motor decline in aging adults.

Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project.

This study characterized person-specific rates of change of total daily physical activity (TDPA) and identified correlates of this change. TDPA metrics were extracted from multiday wrist-sensor recordings from 1083 older adults (average age 81 years; 76% female). Thirty-two covariates were collected at baseline. A series of linear mixed-effect models were used to identify covariates independently associated with the level and annual rate of change of TDPA. Though, person-specific rates of change varied during a mean follow-up of 5 years, 1079 of 1083 showed declining TDPA. The average decline was 16%/year, with a 4% increased rate of decline for every 10 years of age older at baseline. Following variable selection using multivariate modeling with forward and then backward elimination, age, sex, education, and 3 of 27 non-demographic covariates including motor abilities, a fractal metric, and IADL disability remained significantly associated with declining TDPA accounting for 21% of its variance (9% non-demographic and 12% demographics covariates). These results show that declining TDPA occurs in many very old adults. Few covariates remained correlated with this decline and the majority of its variance remained unexplained. Further work is needed to elucidate the biology underlying TDPA and to identify other factors that account for its decline.

Proteome-Wide Discovery of Cortical Proteins That May Provide Motor Resilience to Offset the Negative Effects of Pathologies in Older Adults.

BACKGROUND: Motor resilience proteins have not been identified. This proteome-wide discovery study sought to identify proteins that may provide motor resilience. METHODS: We studied the brains of older decedents with annual motor testing, postmortem brain pathologies, and proteome-wide data. Parkinsonism was assessed using 26 items of a modified United Parkinson Disease Rating Scale. We used linear mixed-effect models to isolate motor resilience, defined as the person-specific estimate of progressive parkinsonism after controlling for age, sex, and 10 brain pathologies. A total of 8 356 high-abundance proteins were quantified from dorsal lateral prefrontal cortex using tandem mass tag and liquid chromatography-mass spectrometry. RESULTS: There were 391 older adults (70% female), mean age 80 years at baseline and 89 years at death. Five proteins were associated with motor resilience: A higher level of AP1B1 (Estimate -0.504, SE 0.121, p = 3.12 × 10-5) and GNG3 (Estimate -0.276, SE 0.068, p = 4.82 × 10-5) was associated with slower progressive parkinsonism. By contrast, a higher level of TTC38 (Estimate 0.140, SE 0.029, p = 1.87 × 10-6), CARKD (Estimate 0.413, SE 0.100, p = 3.50 × 10-5), and ABHD14B (Estimate 0.175, SE 0.044, p = 6.48 × 10-5) was associated with faster progressive parkinsonism. Together, these 5 proteins accounted for almost 25% of the variance of progressive parkinsonism above the 17% accounted for by 10 indices of brain pathologies. DISCUSSION: Cortical proteins may provide more or less motor resilience in older adults. These proteins are high-value therapeutic targets for drug discovery that may lead to interventions that maintain motor function despite the accumulation of as yet untreatable brain pathologies.

From theory to practice: translating the concept of cognitive resilience to novel therapeutic targets that maintain cognition in aging adults.

While the concept of cognitive resilience is well-established it has not been defined in a way that can be measured. This has been an impediment to studying its underlying biology and to developing instruments for its clinical assessment. This perspective highlights recent work that has quantified the expression of cortical proteins associated with cognitive resilience, thus facilitating studies of its complex underlying biology and the full range of its clinical effects in aging adults. These initial studies provide empirical support for the conceptualization of resilience as a continuum. Like other conventional risk factors, some individuals manifest higher-than-average cognitive resilience and other individuals manifest lower-than-average cognitive resilience. These novel approaches for advancing studies of cognitive resilience can be generalized to other aging phenotypes and can set the stage for the development of clinical tools that might have the potential to measure other mechanisms of resilience in aging adults. These advances also have the potential to catalyze a complementary therapeutic approach that focuses on augmenting resilience via lifestyle changes or therapies targeting its underlying molecular mechanisms to maintain cognition and brain health even in the presence of untreatable stressors like brain pathologies that accumulate in aging adults.

Cortical Proteins and Individual Differences in Cognitive Resilience in Older Adults.

BACKGROUND AND OBJECTIVES: Cognitive resilience is a well-recognized concept, but knowledge gaps about its underlying mechanisms have made it difficult to develop instruments that identify older adults with high or low resilience. We tested whether aggregating cortical peptides associated with cognitive resilience into an index can identify adults with higher or lower cognitive resilience. METHODS: We used data from 1,192 older decedents, including annual clinical testing, indices of 10 Alzheimer disease (AD) and related dementia (ADRD) pathologies, and 226 proteotypic peptides measured in the dorsal lateral prefrontal cortex. We used linear mixed-effects models to identify peptides that were related to cognitive resilience (i.e., cognitive decline not explained by ADRD pathologies [false discovery rate <0.05]). We aggregated the expression levels of these resilience peptides into a person-specific cognitive resilience index and examined its association with AD clinical and pathologic phenotypes. RESULTS: We constructed a resilience index from 52 of 226 peptides related to cognitive resilience. A higher index was associated with slower cognitive decline (estimate 0.05, SE 0.003, p < 0.001) and slower motor decline (estimate 0.005, SE 0.001, p < 0.001). Most resilience peptides (70%) were specific to cognitive decline, but 30% also provided resilience for motor decline. A higher index was also related to a lower burden of AD pathologies (odds ratio [OR] 0.41, SE 0.01, p < 0.001) and modified the association of AD pathology with cognition in that a higher index modified the negative effects of AD pathology on AD dementia proximate to death (OR 0.70, SE 0.14, p = 0.010). Up to 90% of cognitive resilience peptides were related to AD pathologic phenotypes. DISCUSSION: Cortical proteins may provide some degree of cognitive resilience. These multifunctional proteins also seem to provide resilience to other AD clinical phenotypes and have independent associations with ADRD pathologies. Resilience proteins may be high-value therapeutic targets for drug discovery of interventions that maintain brain health in aging adults via multiple pathways.

Cross-national harmonization of cognitive measures across HRS HCAP (USA) and LASI-DAD (India).

BACKGROUND: As global populations age, cross-national comparisons of cognitive health and dementia risk are increasingly valuable. It remains unclear, however, whether country-level differences in cognitive function are attributable to population differences or bias due to incommensurate measurement. To demonstrate an effective method for cross-national comparison studies, we aimed to statistically harmonize measures of episodic memory and language function across two population-based cohorts of older adults in the United States (HRS HCAP) and India (LASI-DAD). METHODS: Data for 3,496 HRS HCAP (≥65 years) and 3,152 LASI-DAD (≥60 years) participants were statistically harmonized for episodic memory and language performance using confirmatory factor analysis (CFA) methods. Episodic memory and language factor variables were investigated for differential item functioning (DIF) and precision. RESULTS: CFA models estimating episodic memory and language domains based on a priori adjudication of comparable items fit the data well. DIF analyses revealed that four out of ten episodic memory items and five out of twelve language items measured the underlying construct comparably across samples. DIF-modified episodic memory and language factor scores showed comparable patterns of precision across the range of the latent trait for each sample. CONCLUSIONS: Harmonization of cognitive measures will facilitate future investigation of cross-national differences in cognitive performance and differential effects of risk factors, policies, and treatments, reducing study-level measurement and administrative influences. As international aging studies become more widely available, advanced statistical methods such as those described in this study will become increasingly central to making universal generalizations and drawing valid conclusions about cognitive aging of the global population.

Comparing Performance of Different Predictive Models in Estimating Disease Progression in Alzheimer Disease.

BACKGROUND: Automatic classification techniques provide tools to analyze complex data and predict disease progression. METHODS: A total of 305 cognitively normal; 475 patients with amnestic mild cognitive impairment (aMCI); and 162 patients with dementia were included in this study. We compared the performance of 3 different methods in predicting progression from aMCI to dementia: (1) index-based model; (2) logistic regression (LR); and (3) ensemble linear discriminant (ELD) machine learning models. LR and ELD models were trained using data from cognitively normal and dementia subgroups, and subsequently were applied to aMCI subgroup to predict their disease progression. RESULTS: Performance of ELD models were better than LR models in prediction of conversion from aMCI to Alzheimer dementia at all time frames. ELD models performed better when a larger number of features were used for prediction. CONCLUSION: Machine learning models have substantial potential to improve the predictive ability for cognitive outcomes.

Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults.

BACKGROUND: Methods that can identify subgroups with different trajectories of cognitive decline are crucial for isolating the biologic mechanisms which underlie these groupings. OBJECTIVE: This study grouped older adults based on their baseline cognitive profiles using a latent variable approach and tested the hypothesis that these groups would differ in their subsequent trajectories of cognitive change. METHODS: In this study we applied time-varying effects models (TVEMs) to examine the longitudinal trajectories of cognitive decline across different subgroups of older adults in the Rush Memory and Aging Project. RESULTS: A total of 1,662 individuals (mean age = 79.6 years, SD = 7.4, 75.4%female) participated in the study; these were categorized into five previously identified classes of older adults differing in their baseline cognitive profiles: Superior Cognition (n = 328, 19.7%), Average Cognition (n = 767, 46.1%), Mixed-Domains Impairment (n = 71, 4.3%), Memory-Specific Impairment (n = 274, 16.5%), and Frontal Impairment (n = 222, 13.4%). Differences in the trajectories of cognition for these five classes persisted during 8 years of follow-up. Compared with the Average Cognition class, The Mixed-Domains and Memory-Specific Impairment classes showed steeper rates of decline, while other classes showed moderate declines. CONCLUSION: Baseline cognitive classes of older adults derived through the use of latent variable methods were associated with distinct longitudinal trajectories of cognitive decline that did not converge during an average of 8 years of follow-up.

White matter hyperintensities and cognition across different Alzheimer's biomarker profiles.

BACKGROUND/OBJECTIVES: To examine the association between white matter hyperintensities (WMH) and cognitive domains such as memory and executive function (EF) across different clinical and biomarker categories of Alzheimer's disease (AD). DESIGN: Cross-sectional study. SETTING: Alzheimer's Disease Neuroimaging Initiative. PARTICIPANTS: A total of 216 cognitively normal (CN) participants and 407 participants with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) at baseline. MEASUREMENTS: Based on the 2018 research framework, participants were classified using AT(N) (amyloid-ß deposition [A], pathologic tau [T], and neurodegeneration [(N)]) biomarkers into one of three categories: biomarker negative [A - T- (N)-], amyloid negative but other biomarker positive [A - T ± (N)+ or A - T + (N)±] or amyloid positive [A + T ± (N)±]. Linear regression models were then used to examine the association between WMH and memory composite scores and EF composite scores. RESULTS: Higher WMH burden was associated with worse EF in both CN and MCI subgroups while a significant association between WMH and memory was only found in the MCI subgroup. Furthermore, WMH was associated with EF in the group with A - T ± (N)+ or A - T + (N)± biomarker category, but not for A - T - (N)- (normal biomarker) and A + T ± (N) ± (AD pathology). The association between higher WMH and worse memory was independent of amyloid levels in individuals with MCI with evidence of AD pathology. CONCLUSION: Vascular disease, as indexed by WMH, independent of AD pathology affects cognitive function in both CN and MCI subgroups. Future studies using the AT(N) research framework should consider white matter lesions as a key biomarker contributing to the clinical presentation of AD.

A Coordinated Multi-study Analysis of the Longitudinal Association Between Handgrip Strength and Cognitive Function in Older Adults.

OBJECTIVE: Handgrip strength, an indicator of overall muscle strength, has been found to be associated with slower rate of cognitive decline and decreased risk for cognitive impairment and dementia. However, evaluating the replicability of associations between aging-related changes in physical and cognitive functioning is challenging due to differences in study designs and analytical models. A multiple-study coordinated analysis approach was used to generate new longitudinal results based on comparable construct-level measurements and identical statistical models and to facilitate replication and research synthesis. METHODS: We performed coordinated analysis on 9 cohort studies affiliated with the Integrative Analysis of Longitudinal Studies of Aging and Dementia (IALSA) research network. Bivariate linear mixed models were used to examine associations among individual differences in baseline level, rate of change, and occasion-specific variation across grip strength and indicators of cognitive function, including mental status, processing speed, attention and working memory, perceptual reasoning, verbal ability, and learning and memory. Results were summarized using meta-analysis. RESULTS: After adjustment for covariates, we found an overall moderate association between change in grip strength and change in each cognitive domain for both males and females: Average correlation coefficient was 0.55 (95% CI = 0.44-0.56). We also found a high level of heterogeneity in this association across studies. DISCUSSION: Meta-analytic results from nine longitudinal studies showed consistently positive associations between linear rates of change in grip strength and changes in cognitive functioning. Future work will benefit from the examination of individual patterns of change to understand the heterogeneity in rates of aging and health-related changes across physical and cognitive biomarkers.

A Latent Transition Analysis Model to Assess Change in Cognitive States over Three Occasions: Results from the Rush Memory and Aging Project.

BACKGROUND: Conceptualizing cognitive aging as a step-sequential process is useful in identifying particular stages of cognitive function and impairment. OBJECTIVE: We applied latent transition analysis (LTA) to determine 1) whether the underlying structure of cognitive profiles found at every measurement occasion are uniform across three waves of assessment, 2) whether class-instability is predictive of distal outcomes, and 3) whether class-reversions from impaired to non-impaired using latent modelling is lower than when using clinical criteria of mild cognitive impairment (MCI). METHODS: A mover-stayer LTA model with dementia as a distal outcome was specified to model transitions of ten neuropsychological measures over three annual waves in the Rush Memory and Aging Project (n = 1,661). The predictive validity of the mover-stayer status for incident Alzheimer's disease (AD) was then assessed. RESULTS: We identified a five-class model across the three time-points: Mixed-Domain Impairment, Memory-Specific Impairment, Frontal Impairment, Average, and Superior Cognition. None of the individuals in the Impairment classes reverted to the Average or Superior classes. Conventional MCI classification identified 26.4% and 14.1% at Times 1 and 2 as false-positive cases. "Movers" had 87% increased risk of developing dementia compared to those classified as "Stayers". CONCLUSION: Our findings support the use of latent variable modelling that incorporates comprehensive neuropsychological assessment to identify and classify cognitive impairment.

Predicting Amyloid-ß Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques.

BACKGROUND: Amyloid-ß positivity (Aß+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aß positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. OBJECTIVE: The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aß+ based on gold-standard of PET imaging. METHODS: We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aß status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. RESULTS: The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92. CONCLUSIONS: Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.

Detecting biological heterogeneity patterns in ADNI amnestic mild cognitive impairment based on volumetric MRI.

There is substantial biological heterogeneity among older adults with amnestic mild cognitive impairment (aMCI). We hypothesized that this heterogeneity can be detected solely based on volumetric MRI measures, which potentially have clinical implications and can improve our ability to predict clinical outcomes. We used latent class analysis (LCA) to identify subgroups among persons with aMCI (n = 696) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI), based on baseline volumetric MRI measures. We used volumetric measures of 10 different brain regions. The subgroups were validated with respect to demographics, cognitive performance, and other AD biomarkers. The subgroups were compared with each other and with normal and Alzheimer's disease (AD) groups with respect to baseline cognitive function and longitudinal rate of conversion. Four aMCI subgroups emerged with distinct MRI patterns: The first subgroup (n = 404), most similar to normal controls in volumetric characteristics and cognitive function, had the lowest incidence of AD. The second subgroup (n = 230) had the most similar MRI profile to early AD, along with poor performance in memory and executive function domains. The third subgroup (n = 36) had the highest global atrophy, very small hippocampus and worst overall cognitive performance. The fourth subgroup (n = 26) had the least amount of atrophy, however still had poor cognitive function specifically in in the executive function domain. Individuals with aMCI who were clinically categorized within one group other showed substantial heterogeneity based on MRI volumetric measures.