Journals of Gerontology Series B-Psychological Sciences and Social Sciences最新文献

{"title":"Development of Simple Risk Scores for Prediction of Brain β-Amyloid and Tau Status in Older Adults With Mild Cognitive Impairment: A Machine Learning Approach.","authors":"Kellen K Petersen, Bhargav T Nallapu, Richard B Lipton, Ellen Grober, Christos Davatzikos, Danielle J Harvey, Ilya M Nasrallah, Ali Ezzati","doi":"10.1093/geronb/gbaf085","DOIUrl":"10.1093/geronb/gbaf085","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this work is to use a machine learning framework to develop simple risk scores for predicting β-amyloid (Aβ) and tau positivity among individuals with mild cognitive impairment (MCI).</p><p><strong>Methods: </strong>Data for 657 individuals with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set were used. A modified version of AutoScore, a machine learning-based software tool, was used to develop risk scores based on hierarchical combinations of predictor categories, including demographics, neuropsychological assessments, APOE4 status, and imaging biomarkers.</p><p><strong>Results: </strong>The highest area under the receiver operating characteristic curve (AUC) for predicting Aβ positivity was 0.79, which was achieved by 2 separate models with predictors of age, Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog), APOE4 status, and either Trail Making Test Part B (TMT-B) or white matter hyperintensity. The best-performing model for tau positivity had an AUC of 0.91 using age, ADAS-13, and TMT-B scores, APOE4 information, abnormal hippocampal volume, and amyloid status as predictors.</p><p><strong>Discussion: </strong>Simple integer-based risk scores using available data could be used for predicting Aβ and tau positivity in individuals with MCI. Models have the potential to improve clinical trials through improved screening of individuals.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144013946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insights into the Heterogeneity of Cognitive Aging: A Comparative Analysis of Two Data-Driven Clustering Algorithms.","authors":"Truc Tran Thanh Nguyen, Yu-Ling Chang","doi":"10.1093/geronb/gbaf022","DOIUrl":"10.1093/geronb/gbaf022","url":null,"abstract":"<p><strong>Objectives: </strong>Cognitive aging entails diverse patterns of cognitive profiles, brain imaging, and biomarkers. Yet, few studies have explored the performance of multiple clustering algorithms on a single data set. Here, we employ data-driven methods to analyze neuropsychological performance in older individuals with normal cognition (NC) and mild cognitive impairment (MCI).</p><p><strong>Methods: </strong>A total of 311 older adults without dementia completed a comprehensive assessment, consisting of 17 cognitive tests and a memory complaint questionnaire. We utilized 2 clustering algorithms: nonnegative matrix factorization (NMF) and model-based clustering (MBC). Cluster characteristics were examined in demographic, clinical, and brain morphometric data.</p><p><strong>Results: </strong>Both NMF and MBC uncovered two- and three-cluster solutions, with satisfactory data fit. The two-cluster profiles encompassed a cognitively intact (CI) group and a cognitively suboptimal (CS) group, distinguished by cognitive performance. The 3-cluster solutions included CI-memory proficient, CI-nonmemory proficient, and CS groups. Remarkably, patterns of cognitive heterogeneity and their association with demographic and neuroimaging variables were highly comparable across NMF and MBC. Phenotypic homogeneity improved after identifying participants with consistent and mismatched memberships from the 2 algorithms.</p><p><strong>Discussion: </strong>The results indicate that 2 distinct data-driven algorithms, with different heuristics, generated comparable patterns regarding cognitive heterogeneity within NC and MCI. These findings may inform future subtyping studies in cognitive aging, where replication of stratifications found across different methods is strongly recommended.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"U.S.-Born Older Asians' Diminishing Health Advantage Relative to Other Racial Groups, 2005-2022.","authors":"Leafia Zi Ye, Hui Zheng","doi":"10.1093/geronb/gbaf088","DOIUrl":"10.1093/geronb/gbaf088","url":null,"abstract":"<p><strong>Objectives: </strong>Previous studies have shown that Asian Americans have lower disability and mortality rates than other racial/ethnic groups, indicating a more favorable health profile. This phenomenon is often attributed to the large proportion of Asians being foreign-born and positively selected. However, the health status of U.S.-born older Asians and its trend over time remain unclear.</p><p><strong>Methods: </strong>We used data from the American Community Survey to describe changes in age-adjusted disability prevalence among native-born older Asians relative to other racial/ethnic groups since 2005.</p><p><strong>Results: </strong>Although U.S.-born Asians aged 50 and older had lower disability prevalence than other racial/ethnic groups in 2005-09, their prevalence stagnated over time, while other groups experienced reductions. Consequently, the health advantage of U.S.-born older Asians diminished between 2005 and 2022. A key explanation for this phenomenon is a relative decline in socioeconomic status (SES) among older Asians compared to Whites over time. Asians experienced stagnation in high school attainment and a clear decline in the proportion of the population above the bottom income quintile, while Whites (and most others) experienced improvement in both SES measures. Furthermore, U.S.-born older Asians with low SES experienced an increase in disability, a trend not observed in any other racial or nativity group. We found suggestive evidence that declining community and family support among native-born older Asians may have also eroded their health advantage.</p><p><strong>Discussion: </strong>The \"model minority\" stereotype increasingly misrepresents the well-being of U.S.-born older Asians, a population that requires further research attention.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12246677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Childlessness and Mental Health Among U.S. Older Adults: Do Associations Differ by Marital Status and Gender?","authors":"Deborah Carr, Shinae L Choi","doi":"10.1093/geronb/gbaf073","DOIUrl":"10.1093/geronb/gbaf073","url":null,"abstract":"<p><strong>Objectives: </strong>An estimated 17% of U.S. adults ages 55+ are childless, a fraction that has increased across recent cohorts. Most studies find no or negligible mental health consequences of childlessness for older adults, yet studies typically compare broad categories of childless persons and parents, neglecting potentially important sources of heterogeneity. We evaluate associations between parental status (childless, biological children, stepchildren only, no living children) and 3 dimensions of mental health (depressive symptoms, and social and emotional loneliness) and how these patterns differ by marital status and gender.</p><p><strong>Methods: </strong>Data are from the pooled 2016 and 2018 waves of the Health and Retirement Study (N = 19,354). We estimated ordinary least squares regression models and tested 2- and 3-way interaction terms to evaluate the association between parental status and mental health, and the extent to which these associations are moderated by marital status and gender. Multivariable analyses are adjusted for sociodemographic, social integration, and health covariates.</p><p><strong>Results: </strong>Parental status is not a significant predictor of depressive symptoms in fully adjusted models, and patterns do not differ by marital status and gender. However, men with step-children or biological children report significantly less emotional loneliness relative to childless men, and relative to their female counterparts. Women who have lost all children to death have significantly more emotional loneliness than both their male counterparts and childless women.</p><p><strong>Discussion: </strong>Parental statuses have negligible effects on older adults' mental health; policies and practices to mitigate social isolation should enhance nonfamilial ties and community engagement.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward AI-Driven Precision Measurement of Cognition, Behavior, and Psychological Function in Aging and Alzheimer's Disease and Alzheimer's Disease-Related Dementias.","authors":"Luke E Stoeckel, Dinesh John, Matthew Sutterer","doi":"10.1093/geronb/gbaf045","DOIUrl":"10.1093/geronb/gbaf045","url":null,"abstract":"<p><p>The National Institute on Aging (NIA) is at the forefront of leveraging advances in artificial intelligence (AI) to better understanding of aging and the diagnosis and treatment of Alzheimer's Disease (AD) and Alzheimer's disease-related dementias (ADRD). Recent NIA-supported projects have highlighted the transformative potential of AI, digital health, and computational approaches in improving the modeling, detection, and monitoring of changes in healthy aging and AD/ADRD. This perspective is forward looking, emphasizing key areas and efforts in AI-driven precision measurement in cognition, behavior, and psychological function.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Phenotyping of Cognitive Decline With Retest Learning.","authors":"Zita Oravecz, Joachim Vandekerckhove, Jonathan G Hakun, Sharon H Kim, Mindy J Katz, Cuiling Wang, Richard B Lipton, Carol A Derby, Nelson A Roque, Martin J Sliwinski","doi":"10.1093/geronb/gbaf030","DOIUrl":"10.1093/geronb/gbaf030","url":null,"abstract":"<p><strong>Objectives: </strong>Cognitive change is a complex phenomenon encompassing both retest-related performance gains and potential cognitive decline. Disentangling these dynamics is necessary for effective tracking of subtle cognitive change and risk factors for Alzheimer's Disease and Related Dementias (ADRD).</p><p><strong>Method: </strong>We applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (EAS; n = 316). EAS participants completed multiple bursts of ultra-brief, high-frequency cognitive assessments on smartphones. Analyzing response time data from a measure of visual short-term working memory, the Color Shapes task, and from a measure of processing speed, the Symbol Search task, we extracted several key cognitive markers: short-term intraindividual variability in performance, within-burst retest learning and asymptotic (peak) performance, across-burst change in asymptote and forgetting of retest gains.</p><p><strong>Results: </strong>Asymptotic performance was related to both mild cognitive impairment (MCI) and age, and there was evidence of asymptotic slowing over time. Long-term forgetting, learning rate, and within-person variability uniquely signified MCI, irrespective of age.</p><p><strong>Discussion: </strong>Computational cognitive markers hold promise as sensitive and specific indicators of preclinical cognitive change, aiding risk identification and targeted interventions.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12214872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intergenerational Solidarity and Mental Health in Chinese American Families: A Dyadic Approach.","authors":"Mengting Li, Qun Le, Man Guo, Changmin Peng, Fengyan Tang, Wendi Da, Yanping Jiang","doi":"10.1093/geronb/gbaf079","DOIUrl":"10.1093/geronb/gbaf079","url":null,"abstract":"<p><strong>Objectives: </strong>Existing family and caregiving studies have primarily focused on the mental health of either older adults or adult children. Less is known about the effect of intergenerational relations on the mental health of both generations. This study examined the association between intergenerational solidarity and mental health among older Chinese Americans and their adult children using a dyadic analysis, considering the gendered nature of these relationships.</p><p><strong>Methods: </strong>This study included 214 father-child and 339 mother-child dyads. Intergenerational solidarity (emotional closeness, contact frequency, upward emotional support, upward financial support) and mental health (anxiety, depression, loneliness) were assessed in both generations. Actor-Partner Interdependence Models were used.</p><p><strong>Results: </strong>Greater emotional closeness with their adult children reported by mothers was associated with better mental health in mothers, whereas children's reported emotional closeness with fathers, but not with mothers, was associated with better mental health in children. Daily contact reported by fathers and adult children showed a positive association with their respective mental health. Higher upward emotional support reported by fathers, mothers, and children was associated with mental health in each respective group. Higher upward financial support reported by fathers and mothers was linked to better mental health in each respective group.</p><p><strong>Discussion: </strong>These findings enrich the intergenerational solidarity model by showing how intergenerational solidarity shapes well-being across generations in immigration contexts, varying by solidarity dimension and parental gender. The results suggest that targeted mental health interventions, such as fostering emotional support within immigrant families, may promote well-being for both generations.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12166473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering Key Features of Social Resilience Versus Social Vulnerability in Later Life: A Biopsychosocial Model of Social Asymmetry.","authors":"Hai-Xin Jiang, Jing Yu","doi":"10.1093/geronb/gbaf046","DOIUrl":"10.1093/geronb/gbaf046","url":null,"abstract":"<p><strong>Objectives: </strong>Confronted with shrinking social networks, older adults exhibit individual differences in social adaptability, reflected as socially resilient versus socially vulnerable. The purpose of this study was to examine key features that reflect this social asymmetry in later life.</p><p><strong>Methods: </strong>Three data sets were analyzed, with the training set (N = 424) included older adults from China, whereas 2 test sets (N1 = 2877, N2 = 2343) were from the United States. Social asymmetry was assessed using residuals from a regression of social network on loneliness, with individuals with positive residuals categorized as socially vulnerable and those with negative residuals as socially resilient. Feature selection was performed with the Boruta algorithm, model building with the gradient boosting machine (GBM) algorithm, and model interpretation with the local interpretable model-agnostic explanations (LIME) algorithm.</p><p><strong>Results: </strong>Socially resilient older adults were more prevalent than socially vulnerable ones across datasets from various cultural backgrounds. Five key features-depression, anxiety, stress, sleep disturbance, and personality-were found to predict social asymmetry, with area under the curve (AUC) values ranging from 0.76 to 0.86 across data sets. Older adults with lower levels of depression, anxiety, stress, and sleep disturbance, and typical A or B (vs intermediate) personality, were more likely to be socially resilient.</p><p><strong>Discussion: </strong>The prevalence of socially resilient older adults indicates a relatively positive trend, and most of the key features are plastic and amenable, such as negative emotions and sleep behavior. Developing emotional regulation strategies and providing sleep hygiene education could improve the social adaptability of older adults.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lonely Older Adults in the Era of Social Media: A Meta-Analytic Review.","authors":"Ruoxuan Chen, Kaijie Zhang, Lijuan Cui, Shulin Chen, Ningning Feng","doi":"10.1093/geronb/gbaf080","DOIUrl":"10.1093/geronb/gbaf080","url":null,"abstract":"<p><strong>Objectives: </strong>In an increasingly digital modern society, the loneliness of older adults is a pressing public health concern, and social media is considered a potential solution. Despite past reviews that have attempted to synthesize the association between social media usage (SMU) and loneliness in older adults, the precise connection between the 2 remains unclear. The purpose of this study was to quantify the direct relationship between SMU and loneliness in older adults.</p><p><strong>Methods: </strong>As of August 2023, 3 databases (Web of Science, ProQuest, PubMed) were used for the literature search. A study was included if it measured the relationship between loneliness and SMU in older people over 50 years old. In total, the present study identified 29 effect sizes, representing data from 19 distinct research reports and over 24,877 participants.</p><p><strong>Results: </strong>The meta-analysis applying a random model, shows a weak negative correlation between SMU and loneliness (r = -0.06). The region development moderated the relationship, and specifically, the negative correlation between SMU and loneliness increased to a medium size (r = -0.24) when the samples were in developing regions.</p><p><strong>Discussion: </strong>SMU is negatively correlated with loneliness in old age, which suggests that promotion of SMU among older adults should be implemented, with attention to the inequality between regions and the privacy and availability concerns of older adults. Future research needs to make more efforts in terms of terminology consistency and potential moderators to obtain a more robust understanding of the association between SMU and the loneliness of older adults.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Machine Learning and Environmental and Genetic Risk Factors for the Early Detection of Preclinical Alzheimer's Disease.","authors":"Noor Al-Hammadi, Mahmoud Abouelyazid, David C Brown, Pooja Lalwani, Hannes Devos, David B Carr, Ganesh M Babulal","doi":"10.1093/geronb/gbaf023","DOIUrl":"10.1093/geronb/gbaf023","url":null,"abstract":"<p><strong>Objective: </strong>This study classified preclinical Alzheimer's disease (AD) using cognitive screening, neighborhood deprivation via the area deprivation index (ADI), and sociodemographic and genetic risk factors. Additionally, it compared the predictive accuracy of multiple machine learning algorithms and examined model performance with two bootstrapping procedures.</p><p><strong>Methods: </strong>Data were drawn from a longitudinal cohort that required participants to be age 65 or older, cognitively normal at baseline, and active drivers, defined as taking at least one trip a week. Naturalistic driving data were collected using a commercial datalogger. Biomarker positivity was determined via amyloid pathology using cerebrospinal fluid and positron emission tomography imaging. ADI was captured based on geocoding latitude and longitude to derive a national ranking for the specific location (home or unique destination). Machine learning algorithms classified preclinical AD. Each individual model's predictive ability was confirmed in a 20% testing dataset with 100 rounds of resampling with and without replacement.</p><p><strong>Results: </strong>Among 292 participants (n = 2,792 observations), including ADI of trip destinations, participants' home ADI, and frequency of trips to the same ADI led to a slight but notable improvement in predicting preclinical AD. The ensemble model demonstrated superior predictive performance, highlighting the potential of integrating multiple models for early AD detection.</p><p><strong>Discussion: </strong>Our findings underscore the importance of incorporating socioeconomic and environmental variables, such as neighborhood deprivation, in predicting preclinical AD. Addressing socioeconomic disparities through public health strategies is crucial for mitigating AD risk and enhancing the quality of life for older adults.</p>","PeriodicalId":56111,"journal":{"name":"Journals of Gerontology Series B-Psychological Sciences and Social Sciences","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12223364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}