bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identify Alzheimer’s disease subtypes and markers from multi‐omic data of human brain and blood with a subspace merging algorithm
Authors: Song Ziyan , Huang Xiaoqing , Huang Kun , Zhang Jie 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is a highly heterogeneous disease with different disease trajectory, brain pathological changes, and risk factors. Identifying subtypes of AD can have greatly impact AD diagnosis, treatment, and disease management. Our goal is to identify subtypes of AD by integrating multiple types of omics data from AD patients’ brain samples and blood samples.MethodWe applied a subspace merging algorithm on AD patient brain samples with matched transcriptomic, DNA methylation, and proteomics data collected from the ROSMAP study. This algorithm merges patient graphs generated from these three datasets on a Grassmann manifold followed by spectral clustering. Differential gene expression and pathway analysis were applied to establish the gene signatures between the subtypes (patient clusters) and to the transcriptomic data of the peripheral blood monocyte (PBMC) from the matching patients to identify possible blood markers.ResultOur analysis identified two patient clusters between which the cognitive test scores or the degree of AD pathology are significantly different. Differentially expressed genes between the two groups were identified from both brain and blood samples. Gene set enrichment analysis identified a group of genes significantly enriched with synaptic functions in the brain samples. The Ingenuity Pathway Analysis was used to examine any connections between the significantly differentially expressed genes in the brain samples with the ones identified from the PBMC samples. The low density lipoprotein receptor (LDLR) gene, which was an APOE receptor observed from the blood sample is connected with 12 genes from the brain sample. The results suggested that LDLR could be a novel blood biomarker for AD subtyping.ConclusionThe subspace merging algorithm integrated multimodal omics data from the patients and successfully identified two clusters with distinct clinical progression. Differential expression analysis identified 388 significant genes from the brain samples and 18 from the PBMC samples. The pathway analysis indicated that LDLR could be a novel blood biomarker for AD subtyping. Future work will focus on validating LDLR as a candidate blood marker for AD subtyping using other public datasets with similar study design. The clinical relevance and molecular mechanism basis for the findings will also be investigated.
Social determinants of health and medical factors on brain health in Colombian underserved populations
Authors: Santamaria‐Garcia Hernando , Moguilner Sebastian , Hernandez Hernan , Rodriguez‐Villagra Odir , Ibanez Agustin 
Published: December 2023

AbstractBackgroundGlobal initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in LMICs presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMF), and other medical/social factors predict cognition and functionality in an aging Colombian population.MethodWe ran a cross‐sectional study that combined theory‐ (structural equation models and population attributable risk scores), and data‐driven (machine learning) approaches in a population‐based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality.ResultWe found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status.ConclusionResults highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.
Identification of Persons Living with Dementia Using the Electronic Health Record
Authors: Tan Zaldy S , Qureshi Nabeel , Kremen Sarah , Rhinehart Deana , Gatmaitan Dyane , Spivack Erica , Guinto Augustine , Sicotte Nancy 
Published: December 2023

AbstractBackgroundAlzheimer’s disease and related dementias (ADRD) are underdiagnosed in the primary care setting. Proactive screening for cognitive impairment and subsequent evaluation and diagnosis of patients who screen positive are not routinely and reliably performed. As a result, identification of persons living with dementia (PLWD) for enrollment in clinical programs, clinical trials, and research studies can be challenging. The electronic health record (EHR) represents a potentially viable tool to efficiently and accurate identify PLWD.MethodsIn a large academic health care system in the U.S., we performed an EPIC EHR search for PLWD with the intent of enrolling them in an outpatient dementia care management program. The EHR algorithm used the following criteria for dementia: (1) an ICD‐10 dementia diagnosis in the problem list or past medical history or (2) an FDA‐approved drug on their active medication list. We performed chart review for patients who were identified to have potential dementia using this algorithm to confirm dementia diagnosis is documented in clinical notes.ResultsA total of 985 patients were identified by EHR criteria as potentially having dementia. After chart review, 747 (75.8%) were confirmed to have dementia documented in their clinical history and progress notes. One hundred thirty (13.2%) did not have a confirmed dementia diagnosis, 58 (5.9%) had an incomplete dementia evaluation, and 50 (5.0%) had only mild cognitive impairment. In addition, 55 (5.6%) were deceased.ConclusionWe found that an EHR algorithm can identify patients with dementia in a health plan population. This model may be a useful tool for a focused recruitment of potential participants in clinical trials, research studies, and clinical programs.
The relationship between Event‐Related Potentials and MRI cortical atrophy in a memory disorders clinic
Authors: Morreale Kristina , Marin Anna , Karaca Meltem , Hagan Brenna , Chadalavada Myna , Waskow Emily , DeCaro Renee , Brown Emma , Salat David H , Budson Andrew E , Turk Katherine W 
Published: December 2023

AbstractBackgroundThere is a need for accessible and affordable ways to assess biomarkers of Alzheimer’s disease (AD). Event‐related potentials (ERPs) are associated with disease severity of AD (Fruehwirt et al., 2019) and are a candidate biomarker for AD (Olichney et al., 2022). There is, however, little understanding of how ERP components relate to neurodegeneration of specific brain structures in AD.MethodWe investigated the correlation between electrophysiology, as measured by ERPs and brain structure using quantitative structural MRI measurements using FreeSurfer. A dataset containing older veteran memory disorders patients (N = 25) from VA Boston Healthcare System was analyzed. Participants completed an MRI and an auditory oddball task ERP paradigm.ResultAn exploratory analysis was performed using a Pearson correlation coefficient to evaluate the relationship between ERPs and the thickness and volume of different cortical areas. There was a negative relationship between P300 target amplitude and inferior temporal volume (p < .05), as well as P300 target latency and thickness of the superior temporal (p < .05) and transverse temporal regions (p < .01). P200 standard amplitude had a positive correlation with thickness in the middle temporal (p < .01), and superior temporal regions (p ≤ .05), and P200 target latency had a negative correlation with thickness of the superior temporal region (p < .05). N200 target amplitude had a negative correlation with the superior parietal volume (right hemisphere = p ≤ .05, left hemisphere = p ≤ .01), left inferior temporal volume (p ≤ .05), and the middle temporal volume (left = p ≤ .05, right = p ≤ .01). N200 target latency was negatively correlated with right superior parietal volume (p ≤ .05).ConclusionA correlation was found between ERP measures P200, N200 and P300 with cortical thickness and volume of areas that are commonly impacted by AD pathology, further substantiating use of ERPs as possible AD biomarkers. We conclude that ERPs provide insights as to structural changes of brain structures affected by AD pathology. ERPs could help inform areas of brain atrophy and related patterns of cognitive impairment.
Comparative assessment of α‐synuclein seeding activities in the cerebrospinal fluid of MCI patients phenoconverting to DLB
Authors: Atik Merve , Kondru Naveen Chandra , Savica Rodolfo , Ramanan Vijay K , Graff‐Radford Jonathan , Fields Julie A. , Graff‐Radford Neill R , Day Gregory S. , Ferman Tanis J , Forsberg Leah K. , Boeve Brad F. , Kantarci Kejal , Ross Owen A. 
Published: December 2023

AbstractBackgroundDementia with Lewy bodies (DLB), the second most prevalent form of degenerative dementia, is characterized by pathological α‐synuclein (αSyn) protein aggregation in neurons. Pathological αSyn shows a protein‐seeding phenomenon that may result in aggregation years before clinical symptoms. Studies using Real‐time quaking‐induced conversion (RT‐QuIC) assay show αSyn seeding from peripheral tissue including skin and cerebrospinal Fluid (CSF). Identifying early biomarkers for DLB is essential for accurate diagnosis, and developing effective treatments to slow progression. Herein, we investigate αSyn seeding activities in CSF from patients with mild cognitive impairment with Lewy bodies (MCI‐LB) phenoconverting to DLB.MethodCSF samples were obtained from 3 MCI‐LB and 2 DLB patients at baseline and two‐year follow‐up appointments. CSF samples were tested for their potential to seed α‐synuclein aggregation using an RT‐QuIC assay. CSF samples were tested by seeding 2 μl of CSF in 98 ul of RT‐QuIC reaction mixture with human recombinant αSyn protein, analyzed on a FLUOstar Omega plate reader. Results were recorded every 30 minutes over a period of 45 hours analyzed based on the reaction kinetics to determine the seeding ability of the α‐synuclein in the CSF samples.ResultWe observed seed amplification at first visit for both DLB patients and one MCI‐LB patient. The absolute quantification of aggregation increased at second visit (2 years apart) for one DLB patient and one MCI‐LB. The other two MCI‐LB patients showed no amplification at baseline visit but showed signal at two‐year visit, by which time all three had phenoconverted to DLB. Interestingly the second DLB patient showed a decline at two‐year follow‐up. The medical and neuroimaging records of that patient indicate that the patient’s cognitive decline began as early‐onset disease (first symptoms at age 50 years).ConclusionWe demonstrate that measures of seed aggregation in CSF with RT‐QuIC are informative in MCI‐LB and DLB, and may also reflect changes in disease progression and dynamics.
Cataract, cataract surgery, and risk of incident dementia: a prospective cohort study of 300,823 participants
Authors: Ma Lingzhi 
Published: December 2023

AbstractBackgroundConsidering the increasing rate of cataract surgery and postoperative efficacy, the cataract may be one of the most modifiable risk factors for dementia if surgery reverses the increased risk of dementia caused by cataract.MethodWe used multivariate Cox proportional hazard regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations, with healthy controls as a reference. The same method was used to explore the effects of surgery on the dementia outcomes of cataract patients.ResultMultivariable Cox proportional hazard models showed that the non‐surgical cataract group had a 21.4% increased risk of ACD (HR: 1.214, 95% CI = 1.012‐1.456, P = 0.037) and a 47.9% increased risk of AD (HR: 1.479, 95% CI = 1.105‐1.981, P = 0.009) in the fully adjusted model. However, there was no difference in dementia risk between the cataract surgery group and the healthy control. Cataract surgery was associated with decreased risk of ACD (HR: 0.632, 95% CI = 0.421‐0.947, P = 0.026) and AD (HR: 0.399, 95% CI = 0.196‐0.812, P = 0.011) compared with non‐surgical group.ConclusionOur study supports the hypothesis that cataract surgery reverses the risk of dementia, providing new insights into the prevention and treatment of dementia. Cataract should be considered a significant modifiable risk factor for dementia in dementia prevention strategies.
Perceived Stress and Mindfulness : A Review of Baseline Data for the Practice of Acceptance, Awareness, and Compassion in Caregiving (PAACC) Study
Authors: Sapra Mamta , Hagemann Lauren , Luci Katherine , Savla Jyoti Tina 
Published: December 2023

AbstractBackgroundAs the prevalence of dementia in Veterans is predicted to increase three‐fold in the coming decades, the Veterans Health Administration has made empowering informal caregivers a top priority. Current evidence‐based caregiver interventions, such as Resources for Enhancing All Caregivers Health (REACH‐VA), focus on cognitive and behavioral strategies to lessen caregiver burden. The Practice of Acceptance, Awareness, and Compassion in Caregiving (PAACC) incorporates mindfulness as the main mechanism for reducing caregiver strain. The goal of this intervention is to help caregivers develop increased present‐focused acceptance and awareness of their reactivity to care‐related stressors rather than focus on aspects of care they cannot change.MethodThe study enrolled 127 dementia caregivers with moderate to severe levels of caregiver burden who were randomized to either cognitive behavior based(REACH‐VA) or mindfulness based(PAACC) support intervention. Study assessed caregivers for stress levels, perceived stress, rumination as well as mindfulness using validated questionnaires.ResultsThe mean age of caregivers was 68yrs, with 85% being females and spouses of individuals with dementia. Nearly 75% of the sample have greater than a high school education. Regression analysis of baseline data showed that perceived stress was negatively correlated and associated with mindfulness disposition (b = ‐1.11, p<0.001, R‐square = 0.32). Caregivers benefited from the mindfulness based strategies, and experienced increased acceptance of their caregiver role and greater compassion toward their relative living with dementia.ConclusionWe project that mindfulness will reduce the impact of the perceived stress and rumination, which is often associated with dementia caregiving and improve quality of life for both individual with dementia and their caregivers.
Impaired Glucose Tolerance and Risk for Mild Cognitive Impairment and Dementia:Results from the SPRINT‐MIND Trial
Authors: Williamson Samuel E , Lovato James F , Gaussoin Sarah A. , Reboussin David M. , Williamson Jeff D. 
Published: December 2023

AbstractBackgroundIn epidemiological studies, persons with a diagnosis of diabetes mellitus (DM), especially those with coexisting hypertension, have been shown to be at increased risk for dementia.[1‐3] However, the relationship between impaired glucose tolerance (IGT) and future risk of Mild Cognitive Impairment (MCI) and dementia is less clear, especially in persons without diagnosed DM . The SPRINT‐MIND trial (4) provides an opportunity to assess the relationship between baseline glucose level and risk for transition to mild cognitive impairment in the context of hypertension control.MethodRandomized clinical trial (n = 9,361) conducted at 102 sites in the United States and Puerto Rico among adults aged 50 years or older with hypertension but without diagnosed diabetes or history of stroke. Baseline serum glucose obtained fasting and incidence of adjudicated MCI and probable dementia (PD) was calculated. Results were also stratified by age, baseline cardiovascular disease risk, and baseline systolic blood pressure.ResultAt baseline, 4398 participants (58%) had a serum glucose &lt; 100 mg/dl and 3606 (42%) had a serum glucose &gt;100. Overall, 389 (7.9%) of participants in the normoglycemic group and 250 (6.9%) of participants in the impaired glucose tolerance group developed MCI. For incident PD, 204 (4.1%) and 118 (3.3%) cases occurred in in persons with normal vs. impaired glucose tolerance. Incidence MCI or Dementia did not significantly differ when the results were stratified by baseline age or blood pressure.ConclusionIn persons with hypertension but without diagnosed diabetes, baseline impaired glucose tolerance does not confer elevated risk for MCI or dementia.
Association between Aluminium Levels and Cognitive Decline in Environmentally Exposed Elders
Authors: Gerardo Bianca , Pinto Marina Cabral , Nogueira Joana , Almeida Agostinho , Pinto Edgar , Simões Mário Rodrigues , Freitas Sandra 
Published: December 2023

AbstractBackgroundNormal brain functioning depends on metals homeostasis, as imbalances may increase oxidative stress and cause other detrimental intracellular events that lead to cell loss. Furthermore, environmental exposure to toxic metals is hypothesized to induce neuroinflammation and neuropathology, paving the way to neurodegeneration. The aim of this study was to examine whether such Potentially Toxic Elements (PTE) are associated with cognitive performance over time in a sample of elderly individuals of environmentally contaminated regions.MethodA total of 146 elders permanently residing in environmental risk areas were recruited. Participants completed the Mini Mental State Examination (MMSE) for cognitive assessment and were biomonitored for several PTE through hair and urine analysis. After 5 years, the cognitive assessment was repeated.ResultCognitive performance significantly declined from baseline to follow‐up (p < 0.001), with participants scoring on average 4.41 less points on MMSE (Baseline: M = 25.79, SD = 3.520, Md = 27.00; Follow‐up: M = 21.38, SD = 5.343, Md = 21.00). Regression analysis on the relationship between PTE and cognitive performance revealed that Aluminium (β = ‐0.322, p = 0.32) and Zinc (β = ‐0.372, p = 0.15) in hair and Aluminium in urine (β = ‐0.368, p = 0.010) significantly predicted MMSE scores past 5 years of biomonitoring (p = 0.002). Higher contents of these PTE were associated with worse cognitive performance at follow‐up, after controlling the effects of age, education and supplement intake.ConclusionHigher levels of Aluminium significantly predict worse general cognitive performance 5 years after biomonitoring across different biological samples. Environmental exposure to this metal may constitute a risk factor for cognitive decline.
Prediction of Dementia Using Machine Learning Methods within a Large Pooled Consortia: The Dementia Risk Prediction Pooling (DRPP) Consortium
Authors: Yu Jingzhi , Stephan John , Krefman Amy , Carolan Padraig , Sedaghat Sanaz , Mansolf Maxwell , Soumare Aicha , Gross Alden L. , Aiello Allison , Singh‐Manoux Archana , Ikram M. Arfan , Helmer Catherine , Tzourio Christophe , Satizabal Claudia L. , Levine Deborah A , Lloyd‐Jones Donald , Briceno Emily M , Sorond Farzaneh , Wolters Frank J. , Himali Jayandra Jung , Launer Lenore J J. , Zhao Lihui , Lopez Oscar L. , Seshadri Sudha , Debette Stéphanie , Judd Suzanne E , Hughes Tim M. , Gudnason Vilmundur , Scholtens Denise , Allen Norrina 
Published: December 2023

AbstractBackgroundMachine learning models have been used to create accurate prediction models for dementia. However, many suffer from overfitting and external validation often results in decreased performance. Pooling data from various sources for model training can improve the generalizability of prediction models. We show here a prediction model for dementia developed on pooled data from the Dementia Risk Prediction Pooling (DRPP) Consortium.MethodData from 11 longitudinal disease cohorts within the DRPP and relevant risk factors (25 in total) were collected and harmonized at baseline and follow‐up exams. An ensemble tree‐based algorithm, LightGBM, was used to create two prediction models for dementia at or before 10 years. The first model contains all variables in the dataset, and the second clinical model excludes the Mini‐Mental State Examination (MMSE) score and APOE genotype. 5‐fold cross‐validation was repeated 1000 times to tune the model hyperparameters (number of leaves, tree depth, learning rate) to yield the greatest area under the curve (AUC). Feature importance of the model was analyzed via individual feature information gain. Analysis was performed in R 4.1.2.ResultsAmong the 55,614 participants from 11 cohorts included in this analysis (Table 1), the first model with all the variables had a cross‐validation AUC of 0.762 (CI: 0.757‐0.767). Counterintuitively, the second model of the without the MMSE and APOE variables yielded an AUC of 0.804 (CI: 0.799‐0.809), which may indicate overfitting in the first model. Feature importance analysis show that age is the most important variable in both models. APOE, MMSE, fasting glucose, and any physical activity are the next most important predictors in the full model (Figure 1). In the second model, fasting glucose, any physical activity, gender, and A1c levels were the next most important predictors (Figure 2).ConclusionBy pooling various data sources, we can train machine learning models for dementia risk prediction on more diverse data. Further work is needed to compare the performance of these models with models trained on single data sources via external validation. A pooled dataset also offers an opportunity to understand how model performance will change given shifts in the underlying population.
Evaluation of an intergenerational volunteer program for persons living with dementia and their care partners
Authors: Robitaille Annie , Garcia Linda , Qureshi Muhammad , Adams Michaela , Ducharme Francis 
Published: December 2023

AbstractFor people living with dementia, staying connected and engaging in meaningful activities can have a considerable impact on their quality of life. However, people living with dementia are often subject to dementia‐related stigma putting them at increased risk of social isolation. For care partners who might be struggling emotionally and physically, dementia‐related stigma makes it less likely they will seek help when caring for their loved one. The objective of this study was to evaluate the implementation and effectiveness of person‐centered intergenerational volunteer programs for people living with dementia. More specifically, this study evaluated an intergenerational program that pairs people living with dementia in the community and in long‐term care homes with university and high school students to engage in one‐on‐one friendly interactions with the same students for two hours, twice a week (people living with dementia can also choose to be paired with 2 students to receive more visits per week). Results from data compiled between 2021 and 2023 on 165 students, 54 care partners, and 54 people living with dementia collected at baseline, 4 months, 8 months, and 12 months (i.e., ageism, caregiver burden, quality of life) and feedback about the program collected at 4 months, 8 months, and 12 months will be presented. The implementation, adoption, and sustainability of intergenerational programs will also be discussed. Intergenerational volunteer programs have the potential to improve the quality of life of people living with dementia and to promote dementia‐friendly communities and culture change in long‐term care homes.