Trajectory Of Decline Research Articles

Repeated cognitive assessments show stable function over time in patients with ALS

BackgroundAmyotrophic lateral sclerosis (ALS) is a multisystem disorder with not only motor symptoms but also extra-motor features including cognitive impairment. The most common cognitive profile observed in patients with ALS includes deficits in executive function, language, and social cognition. However, longitudinal studies on cognitive changes over time in ALS are sparse. We aimed to investigate the presence and nature of cognitive impairment at the time of ALS diagnosis and its association with survival as well as explore longitudinal cognitive change.MethodPatients (n = 216) were recruited at the Karolinska University Hospital in Stockholm, Sweden. Follow-up visits (n = 307 in total) were performed every 6 months. Cognitive impairment was assessed using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) and/or Montreal Cognitive Assessment (MoCA).ResultsCognitive impairment was observed in 38% of the patients at the time of ALS diagnosis, and the majority of these patients had deficits in executive function and/or language. Patients with cognitive impairment at the time of diagnosis had a more rapid decline in ALSFRS-R at 12- and 18-months follow-up, and a shorter survival. Cognitive function was stable during the first 2 years after diagnosis, and did not follow the trajectories of decline in motor functions.ConclusionCognitive impairment in ALS was associated with a faster decline of motor functions, and shorter survival. However, cognitive function did not deteriorate over time. Cognitive assessment is essential for the patients and caregivers to understand the phenotypic expression of ALS.

Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer’s type

Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer’s type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer’s Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions—episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.

The progression of cognitive impairment and its influencing factors in older adults based on longitudinal item response theory.

Understanding the development of cognitive impairment and its influencing factors in older adults is crucial for formulating early intervention strategies. To identify the early dimensions of cognitive impairment and provide a comprehensive description of the trajectories of cognitive decline in older adults prior to death. Based on the data of 9883 older adults in the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018, a longitudinal item response theory (Longitudinal IRT) model including covariates was applied to estimate the following parameters. The items in which older adults encountered obstacles first had the least difficulty parameters (δ). The earlier the information curve of an item is lifted, the more information it provides in the early stages of cognitive impairment. Regression coefficient (β) represents the relative rate of cognitive decline. The cognitive impairment values estimated from the Longitudinal IRT were fitted to a mixed-effects model to examine cognitive impairment trajectories. 'Draw the figure on B Card' (δ = -0.816) was the most challenging item, followed by 'recalling 'clothes" (δ = 0.348) and 'recalling 'apples" (δ = 0.419), while 'name the 'pen" (δ = 4.402) was the simplest instruction for the old adults. The curves of the items in the recall dimension began to rise in the early stages of cognitive impairment. Cognitive impairment of older adults who were women (β = 0.061), elder (β = 0.111), smokers (β = 0.060), living in rural areas (β = 0.052), not participating in organised social activities (β = 0.092), suffering from hypertension (β = 0.022), hyperglycaemia (β = 0.035), dyslipidaemia (β = 0.314), low education levels (β = 0.128), manual labourers (β = 0.027), and eventual development of dementia (β = 0.212) exhibited a more accelerated progression. These individuals also had poorer cognitive trajectories. Recall is the earliest dimension of cognitive impairment. The subjects who were women, elder, smokers, living in rural areas, not participating in organised social activities, suffering from hypertension, hyperglycaemia, dyslipidaemia, low education, manual labourers, and eventually development of dementia, had a faster descending speed and poorer trajectories.

Delineating the Heterogeneity of Alzheimer’s Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks

BackgroundAlzheimer’s disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. MethodsWe constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed. ResultsIndividuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate. ConclusionsIn this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.

Markov modeling on dynamic state space for genetic disorders and infectious diseases with mutations: Probabilistic framework, parameter estimation, and applications

Abstract The emergence and dynamic prevalence of genetic disorders and infectious diseases with mutations pose significant challenges for public health interventions. This study investigated the parameter estimation approach and the application of the dynamic state-space Markov modeling of these conditions. Using extensive simulations, the model demonstrated robust parameter estimation performance, with biases and mean-squared errors decreasing as sample size increased. Applying the model to COVID-19 data revealed distinct temporal patterns for each variant, highlighting their unique emergence, peak dominance, and decline or persistence trajectories. Despite the absence of clear trends in the data, the model exhibited a remarkable accuracy in predicting future prevalence trends for most variants, showcasing its potential for real-time monitoring and analysis. While some discrepancies were observed for specific variants, these findings suggest the model’s promise as a valuable tool for informing public health strategies. Further validation with larger datasets and exploration of incorporating additional factors hold the potential for enhancing the model’s generalizability and applicability to other evolving diseases.

Sex Differences in the Trajectories of Cognitive Decline and Affected Cognitive Domains Among Older Adults With Controlled and Uncontrolled Glycemia.

We aimed to analyze the trajectories of cognitive decline as a function of the presence of type 2 diabetes and glycemic control in analyzes stratified by sex in an 8-year follow-up period. A total of 1752 men and 2232 women aged ≥50 years who participated in the English Longitudinal Study of Ageing (ELSA), conducted from 2004 to 2012, were analyzed. The outcomes of interest were performance on the cognitive domains of memory, executive function, and temporal orientation as well as the global cognition score. Cognitive performance was standardized in z-scores in strata based on schooling and age. The participants were classified as without diabetes, with controlled glycemia, and with uncontrolled glycemia, according to medical diagnosis, glucose-lowering medications use and HbA1c levels. Generalized linear mixed models controlled by sociodemographic, behavioral, and health-related characteristics were used for the trajectory analyses. No differences in z-scores were found for global cognition or cognitive domains based on diabetes classification in men and women at baseline. More than 8 years of follow up, women with uncontrolled glycemia had a greater decline in z-scores for global cognition (-0.037 SD/year [95% CI: -0.073; -0.001]) and executive function (-0.049 SD/year [95% CI: -0.092; -0.007]) compared with those without diabetes. No significant difference in trajectories of global cognition or any cognitive domain was found in men as a function of diabetes classification. Women with uncontrolled glycemia are at greater risk of a decline in global cognition and executive function than those without diabetes.

잠재성장모형(LGM)을 이용한 중학생의 자아개념이 창의성과 학습동기에 미치는 영향 종단분석

Objectives This study sought to find ways to improve policies through a longitudinal analysis of the impact of middle school students' self-concept on creativity and learning motivation using the latent growth model (LGM). Methods For this purpose, data on middle school students from the 2nd to 4th years (2019-2021, Panel 2) of the Jeonnam Education Longitudinal Study (JELS) were used for analysis, and self-concept was selected as an independent variable and creativity and learning motivation were selected as dependent variables in the potential growth model. This panel data (JELS) is a 10-year follow-up survey targeting 2,199 students (Panel 1) in 4th grade elementary school from 2018 and 2,549 students in 1st middle school (Panel 2) from 2019. Among the data (Panel 2) for a total of 75 schools, including 58 general middle school schools and 13 innovative schools, starting in 2019, 1,625 students, excluding missing values, were analyzed as final data. Results The main research results are as follows. First, the average self-concept, creativity, and learning motivation of middle school students was around 3 points for self-concept and creativity, and around 2 points for learning motivation, with learning motivation being the lowest. Second, there was a positive correlation between each variable. Third, middle school students' self-concept, creativity, and learning motivation showed a gradual decrease in change over time, and there were individual differences in the initial values of each variable. Fourth, self-concept and creativity showed group differences in initial values depending on gender, but the differences disappeared over time. It is predicted that there will be individual and group differences in learning motivation both at the initial value and over time. Fifth, the initial value and rate of change of self-concept only had a direct effect on the initial value and rate of change of creativity, and the initial value and rate of change of creativity only had a direct effect on the rate of change of learning motivation. Sixth, the initial value and rate of change of creativity had a positive mediating effect on the initial value and rate of change of self-concept and the initial value and rate of change of learning motivation. Learning motivation, which is essential for academic achievement, is highly correlated with and greatly influenced by self-concept and creativity, and the pattern of change shows a trajectory of gradual decline over time. Conclusions Therefore, it is believed that creativity and learning motivation will improve if self-concept is established and improved.

A Longitudinal Study of the Effect of Memory on the Quality of life of European Adults and Older Adults

Among studies conceptualizing quality of life (QoL) as the subjective measure of one’s well-being, evidence suggests a link between cognitive impairment and diminished quality of life. However, the direction of the association is not clear, and most studies have employed a global measure of cognition, which can mask subtle domain-specific declines. In this study, we aim at examining the longitudinal associations between memory and QoL in a representative sample of adults and older adults in Europe and Israel, employing data from the Survey of Health, Ageing and Retirement in Europe (SHARE). The sample was composed by 56,616 respondents aged 50 or older at the beginning of the study period. A Parallel-Process Latent Growth Model (PP LGM) of memory and QoL conditioned on the effects of age, gender, educational level, physical inactivity, depressive symptomatology and social activity participation, was tested. Results displayed a trajectory of decline for both memory and QoL, with higher initial levels of memory associated with higher initial levels of QoL, and steeper decline in memory associated with steeper decline in QoL. Moreover, a positive effect of initial memory onto QoL trend over time was found, indicating that better memory is associated to better QoL in the future, but the opposite did not occur. Results also provide evidence of gender differences. All in all, this work found evidence supporting the longitudinal effect of memory on QoL, which may have consequences for intervention implementation, given that actions for alleviating age-related memory impairment could also have a positive impact onto older adults’ QoL.

Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease

Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p<0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4, acting as a marker of cognitive reserve.

Revealing the heterogeneity of plasma protein and cognitive decline trajectory among Mild Cognitive Impairment patients by clustering of brain atrophy features

Alzheimer's disease (AD) is suggested to be a heterogeneous disorder, but limited studies explore the heterogeneity of the Mild Cognitive Impairment (MCI) stage. This study aimed to tackle such problems using the CIMLR (Cancer Integration via Multikernel Learning) algorithm to cluster brain structural features extracted from T1-weighted Magnetic Resonance Images of MCI patients from Alzheimer's Disease Neuroimaging Initiative. The demographic and cognitive results, characteristics of brain structural features, plasma biomarkers, and longitudinal cognitive trajectory were analyzed for each cluster. The CIMLR clustering analysis revealed four distinct clusters. Cluster 1 is the oldest group but has had mild atrophy and moderate progression with elevated Tumor Necrosis Factor Receptor 2 level; and low Brain-Derived Neurotrophic Factor and CD40 Ligand levels. Cluster 2 showed the highest risk for aggressive MCI progression, with abnormal Leptin, Adiponectin, and Creatine kinase-MB values. Cluster 3 exhibited a low level of Monokine Induced by Gamma Interferon and mild atrophy that shared similar patterns with Cluster 1. Cluster 4 represented the healthiest group during longitudinal tracking, with the mildest Parahippocampal atrophy, which was found to be positively correlated with cognitive impairment and amino acid levels. The longitudinal analyses showed the potential of Hepatocyte Growth Factor as a marker for slow cognitive impairment; Cortisol and Neurofilament Light Polypeptide as prognosis markers for aggressive MCI progression. These findings may lay out new suggestions for further research contributing to the accurate diagnosis and precision medicine for dementia and AD.

Hearing and vision support in people with dementia living at home: Outcomes from the intervention development programme for the European SENSE-Cog Trial

Background Hearing and vision loss are among the most common and disabling comorbidities in dementia and may worsen the trajectory of decline. Improving sensory function may be an accessible and cost-effective means of improving quality of life (QoL) and other outcomes for people with dementia (PwD). Here we describe the outcome of a multi-step intervention development programme for the components and implementation of a cross-national intervention to support hearing and vision in PwD in community settings. Methods We used the process of ‘intervention mapping’ and a set of mixed method sub-studies to develop the intervention for PwD with hearing and/or vision loss. This involved scoping the gaps in understanding, awareness, and service provision, modelling a prototype intervention, refining the prototype into a draft intervention, and finally field trialling the draft intervention for feasibility, acceptability, and tolerability. Input from the ‘patient and public voice’ (PPV) was interlinked with each step of the development programme (Miah et al., 2017). This paper synthesises the results of sub-studies leading to a description and logic model of the intervention which was then evaluated in a fully powered definitive trial. The sub-studies of the programme took place in participants’ own homes and in university or clinic settings in four EU countries: Cyprus, France, Greece, and the UK. The Expert Reference Group took place in Athens, Greece. We used quantitative and qualitative approaches to analyse the data from the different sub-studies. Results A multi-component psychosocial home-based ‘sensory intervention’ designed to optimise hearing and vision in PwD to improve QoL and other dementia-related and care partner-related outcomes. Conclusion This intervention represents the output of the iterative development of a complex intervention to fulfil an unmet need for PwD and sensory loss.

Distinct trajectories of lung function from childhood to mid-adulthood

RationaleLife course trajectories of lung function development and decline influence the risk for lung disease but are poorly documented.ObjectiveTo document lung function trajectories from childhood to mid-adult life.MethodsWe modelled forced...

Exploring trajectories of functional decline and recovery among older adults: a data-driven approach

Independently performing activities of daily living (ADLs) is vital for maintaining one’s quality of life. Losing this ability can significantly impact an individual’s overall health status, including their mental health and social well-being. Aging is an important factor contributing to the loss of ADL abilities, and our study focuses on investigating the trajectories of functional decline and recovery in older adults. Employing trajectory analytics methodologies, this research delves into the intricate dynamics of ADL pathways, unveiling their complexity, diversity, and inherent characteristics. The study leverages a substantial dataset encompassing ADL assessments of nursing home residents with diverse disability profiles in the United States. The investigation begins by transforming these assessments into sequences of disability combinations, followed by applying various statistical measures, indicators, and visual analytics. Valuable insights are gained into the typical disability states, transitions, and patterns over time. The results also indicate that while predicting the progression of ADL disabilities presents manageable challenges, the duration of these states proves more complicated. Our findings hold significant potential for improving healthcare decision-making by enabling clinicians to anticipate possible patterns, develop targeted and effective interventions that support older patients in preserving their independence, and enhance overall care quality.

Dietary E. coli promotes age-dependent chemotaxis decline in C. elegans

An animal’s ability to sense odors declines during aging, and its olfactory drive is tuned by internal states such as satiety. However, whether internal states modulate an age-dependent decline in odor sensation is unknown. To address this issue, we utilized the nematode Caenorhabditis elegans and compared their chemotaxis abilities toward attractive odorants when aged under different dietary conditions. Feeding with the standard laboratory diet, Escherichia coli attenuated the chemotaxis ability toward diacetyl, isoamyl alcohol, and benzaldehyde when aged. On the other hand, feeding with either the lactic acid bacteria Lactobacillus reuteri or food deprivation selectively maintained the chemotaxis ability toward diacetyl. Our results suggest that ingestion of E. coli causes age-dependent chemotaxis decline. The changes in the chemotaxis behavior are attributed to the different expressions of diacetyl receptor odr-10, and the chemotaxis behavior of aged animals under food deprivation is shown to be dependent on daf-16. Our study demonstrates the molecular mechanism of how diet shapes the trajectory of age-dependent decline in chemosensory behaviors.

Declining calcium concentration drives shifts toward smaller and less nutritious zooplankton in northern lakes.

Zooplankton community composition of northern lakes is changing due to the interactive effects of climate change and recovery from acidification, yet limited data are available to assess these changes combined. Here, we built a database using archives of temperature, water chemistry and zooplankton data from 60 Scandinavian lakes that represent broad spatial and temporal gradients in key parameters: temperature, calcium (Ca), total phosphorus (TP), total organic carbon (TOC), and pH. Using machine learning techniques, we found that Ca was the most important determinant of the relative abundance of all zooplankton groups studied, while pH was second, and TOC third in importance. Further, we found that Ca is declining in almost all lakes, and we detected a critical Ca threshold in lake water of 1.3 mg L-1 , below which the relative abundance of zooplankton shifts toward dominance of Holopedium gibberum and small cladocerans at the expense of Daphnia and copepods. Our findings suggest that low Ca concentrations may shape zooplankton communities, and that current trajectories of Ca decline could promote widespread changes in pelagic food webs as zooplankton are important trophic links from phytoplankton to fish and different zooplankton species play different roles in this context.

Sex differences in cognitive function trajectories and their determinants in older adults: Evidence from the Chinese longitudinal healthy longevity survey.

To examine sex differences in the cognitive trajectories of a nationally representative sample of older people living in China and to explore potential determinants of these trajectories. The study included 2230 women and 2171 men who were cognitively healthy and aged over 60 at the first observation from the Chinese Longitudinal Healthy Longevity Survey based on the 2008-2018 cohort. Cognitive function was measured using the Chinese version of the Mini-Mental State Examination (MMSE). Group-based trajectory modeling was used to identify potential heterogeneity of longitudinal changes over the 10years in each gender. Logistic regression was used to investigate associations between baseline characteristics (age, education, fertility history, sleep length, physical activity, and health status and behaviors) and trajectory classes. Three trajectories (labeled stable, slow decline, and rapid decline) were identified according to the changes in MMSE scores for both women and men. For the women, both the slow and rapid decline groups accounted for a larger proportion (14.7% and 11.0%, respectively) than the male decline groups (8.1% and 6.6%, respectively), and the women had a lower baseline MMSE score with a faster decline. In the multivariable logistic regression analyses, older age, less education, older age at first birth, poorer functional abilities, hearing impairment, and lower baseline MMSE scores were significantly associated with cognitive decline in both the female and male groups compared to the stable group. For the women, sleeping over 9h was also associated with a rapid cognitive decline trajectory, while current exercise and being overweight/obese were protective factors against cognitive decline. The women had an overall more serious cognitive decline than men. The potential determinants of cognitive decline identified in this study could be considered for developing specific intervention strategies aimed at promoting a healthy brain and preventing cognitive decline in different sexes, especially in low-income and developing countries.

The SNP rs6859 in NECTIN2 gene is associated with underlying heterogeneous trajectories of cognitive changes in older adults

BackgroundFunctional decline associated with dementia, including in Alzheimer’s disease (AD), is not uniform across individuals, and respective heterogeneity is not yet fully explained. Such heterogeneity may in part be related to genetic variability among individuals. In this study, we investigated whether the SNP rs6859 in nectin cell adhesion molecule 2 (NECTIN2) gene (a major risk factor for AD) influences trajectories of cognitive decline in older participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).MethodsWe retrospectively analyzed records on 1310 participants from the ADNI database for the multivariate analysis. We used longitudinal measures of Mini-Mental State Examination (MMSE) scores in participants, who were cognitively normal, or having AD, or other cognitive deficits to investigate the trajectories of cognitive changes. Multiple linear regression, linear mixed models and latent class analyses were conducted to investigate the association of the SNP rs6859 with MMSE.ResultsThe regression coefficient per one allele dose of the SNP rs6859 was independently associated with MMSE in both cross-sectional (-2.23, p < 0.01) and linear mixed models (-2.26, p < 0.01) analyses. The latent class model with three distinct subgroups (class 1: stable and gradual decline, class 2: intermediate and late decline, and class 3: lowest and irregular) performed best in the posterior classification, 42.67% (n = 559), 21.45% (n = 281), 35.88% (n = 470) were classified as class 1, class 2, and class 3. In the heterogeneous linear mixed model, the regression coefficient per one allele dose of rs6859 – A risk allele was significantly associated with MMSE class 1 and class 2 memberships and related decline; Class 1 (-2.28, 95% CI: -4.05, -0.50, p < 0.05), Class 2 (-5.56, 95% CI: -9.61, -1.51, p < 0.01) and Class 3 (-0.37, 95% CI: -1.62, 0.87, p = 0.55).ConclusionsThis study found statistical evidence supporting the classification of three latent subclass groups representing complex MMSE trajectories in the ADNI cohort. The SNP rs6859 can be suggested as a candidate genetic predictor of variation in modeling MMSE trajectory, as well as for identifying latent classes with higher baseline MMSE. Functional studies may help further elucidate this relationship.

Trajectories of cognitive decline in different domains prior to AD onset in persons with mild cognitive impairment

ObjectivesTo explore the trajectories and the change-points of global and five domain-specific cognitive functions before the onset of Alzheimer's disease (AD). MethodsData was retrieved from the Alzheimer's Disease Neuroimaging Initiative with follow-up from 2005 to 2022. Participants with mild cognitive impairment (MCI) at baseline and those who progressed to AD during follow-up were included. The time of AD onset was defined as the visit time when participant was first diagnosed as AD during follow-up. Global and five domain-specific cognitive functions (immediate memory, visuospatial ability, language, processing speed and executive function) were assessed by Mini-Mental State Examination, Immediate recalling trials of Rey Auditory Verbal Learning Test, Clock Drawing Test, Animal Fluency Test, Part A and B of Trail Making Test, respectively. Their trajectories and change-points before AD onset were explored by generalized additive mixed models and piecewise linear regression models, respectively. Results349 participants were diagnosed as MCI at baseline and converted to AD during follow-up, who were included in this study. They had been visited on an average of 4.6 times (SD = 2.1, range = 2.0–13.0), with a total of 1593 visits. Their mean baseline age and AD onset age were 74.4 (SD = 6.4, range = 60.0–88.4) and 77.0 (SD = 6.8, range = 60.5–94.7) years, respectively. Baseline age and educational year were significantly associated with global cognitive, immediate memory, language and executive function. Men presented better global cognitive function (β = 0.54, p < 0.05) but poorer immediate memory (β = −1.72, p < 0.05) than women. Immediate memory and visuospatial ability showed the earliest change-points at 4 years before the onset of AD (Note as T-4years), followed by language (T-3.5years), executive function (T-2.5 years), processing speed (T-2.0 years), and finally the global cognitive function (T-1.5years). ConclusionsThe trajectories of the six neuropsychological scores were non-linear and showed deterioration in functions over time. Immediate memory and visuospatial ability showed the earliest change-points prior to AD onset.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals

Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Individuals WODCI at baseline scan. Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis

ObjectiveThis study aimed to utilize data-driven machine learning methods to identify and predict potential physical and cognitive function trajectory groups of older adults and determine their crucial factors for promoting active ageing in China. MethodsLongitudinal data on 3026 older adults from the Chinese Longitudinal Healthy Longevity and Happy Family Survey was used to identify potential physical and cognitive function trajectory groups using a group-based multi-trajectory model (GBMTM). Predictors were selected from sociodemographic characteristics, lifestyle factors, and physical and mental conditions. The trajectory groups were predicted using data-driven machine learning models and dynamic nomogram. Model performance was evaluated by area under the receiver operating characteristics curve (AUROC), area under the precision-recall curve (PRAUC), and confusion matrix. ResultsTwo physical and cognitive function trajectory groups were determined, including a trajectory group with physical limitation and cognitive decline (14.18 %) and a normal trajectory group (85.82 %). Logistic regression performed well in predicting trajectory groups (AUROC = 0.881, PRAUC = 0.649). Older adults with lower baseline score of activities of daily living, older age, less frequent housework, and fewer actual teeth were more likely to experience physical limitation and cognitive decline trajectory group. LimitationThis study didn't carry out external validation. ConclusionsThis study shows that GBMTM and machine learning models effectively identify and predict physical limitation and cognitive decline trajectory group. The identified predictors might be essential for developing targeted interventions to promote healthy ageing.