bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of loss of occlusal support with 15‐year risk of dementia
Authors: Asher Sam , Suominen Anna Liisa , Stephen Ruth , Ngandu Tiia , Koskinen Seppo , Solomon Alina 
Published: June 2023

AbstractBackgroundRecent studies link tooth loss with dementia, however, the mechanism that underlies this association is not known. One hypothesis attributes this association to loss of sensory stimulation from the masticatory apparatus. Since establishment of occlusal contacts between the opposing teeth enables functionality (e.g., mastication, etc.) as well as supports the facial structure by maintaining static occlusal contacts, hence, this study examines the effect of loss of occlusal support (number of occlusal pairs) on dementia risk.MethodThis study included 4013 dementia‐free participants (1880 males, 2133 females), aged 30‐93 years (mean 47.23 years) from Finnish Health 2000 survey. Tooth count and number of occlusal pairs (maximum 10 pairs) were recorded in oral clinical examination. Dementia diagnosis up till 2015 was retrieved from hospital discharge and causes of death registers. Cox regression models were generated to assess 15‐year dementia risk against loss of occlusal pairs. Association was adjusted for age, gender, years of education, cardiovascular risk, socioeconomic status, dietary index, depression, and overall tooth count. Analysis was further expanded by stratifying number of occlusal pairs into having ≥8 pairs, 4‐7 pairs, and ≤3 pairs. Sensitivity analysis was conducted for participants aged ≥55 years.Result137 participants developed dementia over 15 years. Fully adjusted model indicated 13% higher risk of dementia for each occlusal pair lost (Model 3). Further adjustment for overall tooth count increased dementia risk to 21% (Model 4). In stratified analysis, having ≤7 occlusal pairs carried significantly higher risk of dementia. This association appeared to be dose‐dependent, with having 4‐7 pairs carrying twice the dementia risk while having ≤3 pairs carrying almost three times the dementia risk in comparison to having ≥8 pairs. The findings were corroborated by sensitivity analysis for each occlusal pair lost. In stratified sensitivity analysis having ≤3 pairs was associated with higher risk of dementia but hazards ratio for having 4‐7 pairs failed to achieve statistical significance.ConclusionPreliminary results indicate that loss of occlusal support/number of occlusal pairs independently increases dementia risk. Further in‐depth studies assessing the impact of particular tooth types lost and the possible mechanisms are warranted.
Transferable structural, static and dynamic functional MRI features for predicting conversion from mild cognitive impairment to Alzheimer’s disease in Shanghai Memory Study and ADNI cohorts
Authors: Chen Zhihan , Chen Ke‐Liang , Li Yuxin , Geng Daoying , Li Xiantao , Liang Xiaoniu , Lu Huimeng , Ding Saineng , Xiao Zhenxu , Ma Xiaoxi , Zheng Li , Ding Ding , Zhao Qianhua , Yang Liqin 
Published: June 2023

AbstractBackgroundMild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer’s disease (AD) with high risk of conversion. It is of great challenge to construct reliable biomarkers for predicting conversion from MCI to AD, while the underlying mechanism is still not fully explored. Inter‐dataset generalizability is a prerequisite for clinical use of biomarkers and always a shortage of neuroimaging‐based studies.MethodIn this study, we propose a novel framework by integrating structural MRI (sMRI) and both static and dynamic resting‐state functional MRI (fMRI) measurements to investigate the differences between MCI converters (MCI_C) and non‐converters (MCI_NC), and then utilized support vector machine (SVM) to construct the prediction models based on selected features. A total of 186 MCI patients with both MRI and three‐year outcome data were selected from two independent cohorts: Shanghai Memory Study (SMS) cohort for selection of MRI predictors and internal cross‐validation, and ADNI cohort for external validation on the generalizability of these MRI predictors.ResultIn comparison with MCI_NC, the MRI converters were mainly characterized by alterations of medial temporal lobe (MTL) with atrophy extending to lateral temporal and regional hyperactivity and instability, posterior parietal cortex (PPC) with atrophy and inter‐regional hypo‐connectivity and connectional instability, and occipital cortex with functional instability. All of the imaging‐based prediction models achieved an AUC above 0.7 and ACC above 70% in both SMS and ADNI cohorts. The combination of static and dynamic fMRI features resulted in overall good performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features into the fMRI model. In both cohorts, the best imaging model was the multi‐modality MRI model which provided excellent performance with AUC above 0.85 and average ACC/sensitivity/specificity around 80%.ConclusionThis inter‐cohort validation study provides a new insight into the mechanisms of MCI conversion and paves a way for eventual clinical use of MRI biomarkers.
Prevalence and progression of subjective cognitive decline among rural Chinese older adults: A Population‐based follow‐up study
Authors: Xu Shan , Ren Yifei , Liu Rui , Li Yuanjing , Hou Tingting , Wang Yongxiang , Wang Lidan , Cong Lin , Du Yifeng , Qiu Chengxuan 
Published: June 2023

AbstractBackgroundFew studies have examined occurrence and progression of subjective cognitive decline (SCD) in rural China. We aimed to investigate the prevalence of SCD and explore factors associated with progression from SCD to cognitive impairment, no dementia (CIND) and Alzheimer’s disease (AD).MethodThis community‐based study included 2488 cognitively unimpaired participants (age ≥65 years; 55.18% women; and 39.87% illiterate) in the Shandong Yanggu Study of Aging and Dementia who were examined at baseline (2014‐2015), of these, 1843 were followed in 2018. Demographic, epidemiological, clinical, and neuropsychological data were collected following a structured questionnaire. SCD was defined based on the Chinese version of the self‐rated Ascertain Dementia 8‐item Questionnaire (AD8) score. CIND and AD were diagnosed following the international criteria. Data were analyzed with the multivariate logistic regression models.ResultAt baseline, 997 of the 2488 participants were defined to have SCD. The prevalence of SCD was 40.07%, and the prevalence was increased with age. Of the 1843 participants in the follow‐up examination in 2018, 189 were diagnosed with CIND and 58 with AD. Having SCD at baseline was significantly associated with the multi‐adjusted odds ratio (OR) of 1.55 (95% confidence interval = 1.14‐2.12) for incident CIND and 2.91 (1.56‐5.42) for incident AD. Of 745 participants with SCD at baseline, 97 progressed to CIND and 43 to AD during the average 3.79 years of follow‐up. Multivariable logistic regressions showed that the multi‐adjusted OR of incident CIND was 0.51 (0.26‐0.98) for high social support, whereas the multi‐adjusted OR of incident AD was 1.22 (1.14‐1.31) for older age (per 1‐year increment), 0.31 (0.11‐0.85) for high education, 2.77 (1.17‐6.57) for carrying the apolipoprotein E (APOE) ε4 allele, 0.29 (0.11‐0.75) for high social support, and 3.98 (1.37‐11.52) for mild depressive symptoms.ConclusionSCD affects over 40% of rural‐dwelling older adults in China who are free of cognitive impairment. Older people with SCD are more likely to develop CIND and AD. Older age, illiteracy, APOE ε4 allele, low social support, and depressive symptoms are associated with accelerated progression from SCD to CIND or AD.
Injurious falls before, during, and after dementia diagnosis: A population‐based study
Authors: Zhang Lulu , Wang Jiao , Dove Abigail , Yang Wenzhe , Li Xuerui , Qi Xiuying , Xu Weili 
Published: June 2023

AbstractBackgroundFalls are common among older adults with dementia, but the timing of incident injurious falls at different stages of dementia diagnosis is unclear. We aimed to examine injurious falls before, during, and after dementia diagnosis to detect when the occurrence of falls starts to increase prior to dementia onset, when people with dementia are at highest risk of falls, and which factors are associated with falls among people with dementia.MethodThis study included 4874 participants aged ≥55, consisting of 2437 incident dementia cases detected in a dementia‐free cohort from the Swedish Twin Registry and 2437 1:1 matched (i.e., same birth year and sex) non‐demented controls from the cohort. Dementia diagnosis and date of onset were identified from the National Patient Registry (NPR) and the Swedish Cause of Death Register. Information on injurious falls, including date of occurrence and history of chronic disease, was obtained from the NPR. Data on demographics, lifestyle, and anthropometrics were collected through a self‐reported questionnaire. Data were analyzed using conditional Poisson regression and generalized estimating equation (GEE) models.ResultIn Poisson regression analysis, the adjusted incidence rates of injurious falls among dementia cases and controls were similar (around 3.65‐9.08 per 1,000 person‐years) in the 6‐10 years preceding dementia diagnosis. Compared to controls, the incidence of falls in dementia cases started to increase beginning 5 years prior to dementia diagnosis (incidence rate ratio [IRR] 1.50, 95% confidence interval [CI] 1.11‐2.03), reaching a peak (IRR 3.37, 95% CI 2.85–3.97) in the year of dementia diagnosis, and declining rapidly 3 years after dementia diagnosis (IRR 1.38, 95% CI 1.05–1.79). In multi‐adjusted GEE models, alcohol consumption, living alone, and stroke were significantly associated with incident falls among people with dementia.ConclusionCompared to dementia‐free people, those with dementia have a greater incidence of injurious falls that increases in the 5 years leading up to dementia diagnosis and peaks during the year of dementia diagnosis. Alcohol consumption, living alone, and stroke may predict falls among people with dementia. Our findings highlight the importance of a tailored preventive strategy against falls among people with dementia, especially in the diagnosis stage.
Prediction of the 3‐year survival in patients with cognitive impairment based on demographics, neuropsychological data, and comorbidities
Authors: Xing Dianxia , Zhang Wenbo , Yi Qingjie , Hong Huang , Chen Lihua , Wu Jiani , Yu Weihua , Lü Yang 
Published: June 2023

AbstractBackgroundTo develop and validate a prediction model for the 3‐year survival of patientwith cognitive impairment based on demographics, neuropsychological assessment data, and comorbidities.MethodFive‐hundred‐and‐eighty‐seven patients with cognitive impairment were included from our memory clinic. Demographic information, comorbidities, the Mini‐Mental State Examination (MMSE), Instrumental Activity of Daily Living (IADL) score, and Neuropsychiatric Inventory Questionnaire sub‐item scores were collected. Survival conditions were obtained by telephone from the caregivers in the third year after the neuropsychological tests. Patients were randomly divided into training set and validation set with a ratio of 7:3. Variables that achieved statistical significance (P < 0.05) in the single‐factor analysis were incorporated in the binary logistic regression model. A nomogram was established according to the multivariate analysis. The receiver operating characteristic curve, area under the curve, C‐index, calibration curve, and the Hosmer–Lemeshow test were performed to validate the nomogram.ResultYounger age, higher MMSE score, lower IADL score, no symptom of disinhibition, and Charlson Comorbidity Index score equal or less than 1 are associated with 3‐year survival. These predictors yielded good discrimination with C‐indexes of 0.80 (0.73–0.87) and 0.85 (0.77–0.94) in the training and validation cohorts, respectively. The Hosmer–Lemeshow test showed no statistical significance in either cohort, and calibration curves displayed a good consistency between the prediction and the actual observation.
Cognitive function level and chronic disease incidence rate by age of older adults living in the community
Authors: Nam Sanghun , Park Sojung , Bae Suyeong , Hong Ickpyo 
Published: June 2023

AbstractBackgroundResearch has shown that decline in cognitive function is related to the level of chronic disease in old age. Most studies, however, have either examined limited aspects of cognitive function or used assessment tools with low accuracy. A limited range of cognitive function items and low‐accuracy cognitive function assessment tools may bias the findings. Current literature also has paid little attention to the age‐gradient association between cognitive function and chronic diseases. Utilizing a new cognitive classification method, this study investigated cognitive function‐associated chronic diseases and their relationship with age. Empirical knowledge about the dynamic among cognitive function, changes in chronic diseases, and age may contribute to establishing comprehensive prevention and management programs of diseases in later years.MethodA total of 6,940 people aged between 55 to 105 years from the 2018 Korean Longitudinal Study of Aging panel data were included in this study. According to the Korean Mini‐Mental State Examination’s total score and instrumental activities of daily living assistance, cognitive status was classified into normal cognition, moderate cognitive impairment, and severe cognitive impairment. The Poisson regression analysis was used to calculate the incident rate ratios (IRR) of cognitive status by age and the incidence of chronic diseases.ResultThe incidence rate of chronic diseases was 1.07 times higher in the moderate cognitive impairment group (n = 4665) than in the normal cognitive group (n = 4665). In addition, it was 1.24 times higher in the severe cognitive impairment group (n = 420) than in the normal cognitive group (Table 1) and the incidence of chronic diseases according to cognitive function increased with age (figure 1).ConclusionApplying the new cognitive function classification, we found the incidence rate of age‐related chronic diseases increased with the cognitive status, indicating incidence of chronic diseases increases as the cognitive state decreases and the age increases, leading to complex chronic conditions. This finding points to the importance of an integrated program of cognitive decline prevention and the chronic disease management program, which are currently developed and implanted separately.
Health‐related lifestyle behaviors predict changes in cognitive status in older adults: A latent transition analysis of data from the Mexican Healthy Aging Study 2012‐2018
Authors: Robertson Michael C , Milani Sadaf Arefi , Downer Brian , Samper‐Ternent Rafael , Lyons Elizabeth J , Schulz Paul E 
Published: June 2023

AbstractBackgroundHealth behaviors, such as exercise, smoking, and alcohol consumption, are potentially modifiable risk factors for cognitive impairment and dementia. The effect of these behaviors on cognitive function is important to examine in Mexico, which faces a rapidly aging population.MethodData came from the 2012, 2015, and 2018 waves of the Mexican Health and Aging Study. The final sample included 8,112 participants aged 60 and older. We used latent transition analysis (LTA) to identify latent statuses of cognitive functioning using standardized scores for five cognitive tasks (orientation, visual scanning, verbal learning, delayed verbal recall, and verbal fluency). We evaluated associations between baseline lifestyle behaviors and transition probabilities for latent statuses of cognitive functioning across observation waves.ResultWe identified a three‐status model across the three waves with support for measurement and stationary invariance. Latent statuses were characterized by low (24% at baseline), average (55%), and high cognitive abilities (21%). With probabilities of .66, .78, and .90, respectively, individuals’ statuses tended to stay the same between time periods. The probabilities of an individual with these statuses transitioning to death were .15, .07, and .02, respectively. For individuals with a low cognition status, reporting exercising or doing hard physical work three or more times a week on average during the two years preceding baseline was associated with lower odds of transitioning to either death (OR = 0.551, p<.001) or a high cognition status (OR = 0.006, p<.001), in relation to staying in the same status. Similarly, alcohol use for this group was associated with decreased odds of transitioning to either death (OR = 0.74, p = .040) or a high cognition status (OR = 0.005, p<.001). Finally, for individuals with a low cognition status, reporting reading regularly was associated with increased odds of transitioning to average cognition status (OR = 1.44, p<.046), in relation to staying in the same status.ConclusionFindings suggest that individuals tended to stay in the same cognitive status over time and that reading regularly predicted favorable changes in cognition. LTA may be useful for identifying discreet statuses and subsequent transitions in older adults’ cognitive functioning as determined by performance on cognitive tasks.
Assessing the effects of a longitudinal buddy program on mood, knowledge and attitudes toward aging and dementia in university students
Authors: Nambiar Anjali , Neuville Raumin S. , Lee Ariel , Phung Calvin , Grill Joshua D. , Torabzadeh Simin 
Published: June 2023

AbstractBackgroundThe population of Americans 65 and older is growing rapidly and at risk for Alzheimer’s disease and related disorders (ADRD). As a result, younger generations will be challenged with addressing increasing demand in care for the growing geriatric population. Previous studies have explored the impact of volunteer programs involving geriatric residents and improvements in mood and knowledge of aging in medical and high school students. They underscore the importance of intergenerational relationships, but programs involving university students and individuals with ADRD have not been widely studied.MethodsAlzheimer’s Buddies at UC Irvine (ABUI) pairs trained university student volunteers with an older individual, or “buddy,” with ADRD. ABUI recruited 30 volunteers total to participate in weekly activities with their buddies for one hour. Considering COVID‐19 safety guidelines, current volunteers have interacted virtually with buddies via phone calls or video conferencing. Volunteers submit reflection journals following each visit describing activities completed, lessons learned, and challenges faced during the visit. Before and after 10 weeks of participating in ABUI, new volunteers’ mood, attitudes toward the elderly, and knowledge of ADRD will be assessed using the following surveys: Dementia Knowledge Assessment Scale, Kogan’s Attitudes Toward Old People Scale, Perceived Stress Scale, and Family Stigma in Alzheimer’s Disease Scale.ResultsWe will assess changes in newly recruited volunteers’ mood, knowledge, and attitudes toward ADRD after 10 weeks of participating in ABUI based on the surveys administered. The 160 initial journal submissions from current volunteers demonstrate that volunteers explored shared interests and observed improvement in their buddies’ demeanor over multiple visits. Additionally, volunteers noted difficult situations and strategies developed to adapt and optimize their interactions despite these obstacles.ConclusionABUI provides university students an opportunity to learn more about ADRD and develop their own viewpoints on this unique and growing population. Preliminary findings in volunteer reflection journals provide insight into the program’s effect on volunteers and will be followed by a more rigorous assessment. The results of this study may show that consistent long‐term interaction could be rewarding and provide a better understanding of the experiences of individuals with ADRD for future care providers.
Transfer learning based deep encoder decoder network for amyloid PET harmonization with small datasets
Authors: Shah Jay , Chen Kewei , Reiman Eric M. , Li Baoxin , Wu Teresa , Su Yi 
Published: June 2023

AbstractBackgroundThe existence of multiple amyloid tracers with varied characteristics poses a significant challenge to standardized interpretation and quantification of amyloid PET data. We previously demonstrated that a deep learning model using a residual inception encoder‐decoder network (RIED‐Net) architecture improved harmonization of florbetapir (FBP) and PiB PET. However, scarcity of head‐to‐head comparison datasets of other amyloid PET tracer may limit the ability to generalize this approach to the other amyloid PET tracers. In this research, we investigate the performance of RIED‐Net model trained on smaller datasets and explore transfer learning (TL) approaches which can leverage multiple different datasets and address the data scarcity issue.MethodWe previously trained our RIED‐Net model using PiB‐FBP data from Open Access Series of Imaging Studies (OASIS) (N = 92) and tested the performance in the independent Global Alzheimer’s Association Interactive Network (GAAIN) (N = 46) dataset. We now use the same procedure to train the RIED‐Net model using the smaller GAAIN dataset and evaluate the model performance. We then examine the feasibility of a TL approach by comparing the model performance between RIED‐Net model directly trained on the PiB‐florbetaben (FBB) from GAAIN (N = 35) and the TL approach in which we started with the optimal RIED‐Net model for PiB‐FBP data and fine‐tuned the network parameters using the GAAIN PiB‐FBB data. A cross‐validation (CV) approach was used to evaluate model performance based on difference before and after harmonization in terms of correlation between mean cortical SUVR (mcSUVR) measurements from different tracers.ResultRIED‐Net model trained using the smaller GAAIN PiB‐FBP data improved the agreement of mcSUVR measurements as measured by Pearson correlation from r = 0.93 to r = 0.96 (p<0.01) on test‐set. For PiB‐FBB harmonization, the TL approach provided numerically better agreement in mcSUVR from r = 0.96 (direct train) to r = 0.97 (with TL), demonstrating the potential benefits of applying TL based technique to address the issue of smaller sample size.ConclusionWe demonstrate our RIED‐Net model can achieve improved harmonization even when trained on a relatively small training set. We also demonstrate the potential of transferring knowledge learned from one tracer pairs to improve the harmonization model of other related tracer pairs.