bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between Medical Comorbidities and White Matter Hyperintensity Volume in Diverse Populations: A HABS‐HD Study
Authors: Trammell Antoine R , Lah James J. , Parker Monica W , Goldstein Felicia , Hales Chadwick M , Davis Crystal P , Dorbin Cornelya D , Saleh Sabria , Murphy‐Tchivzhel Elizabeth , Rose Alecia Michella , Osborne Amber , Ahmad Adiva , Al‐Amin Jahmila , O'Bryant Sid E. 
Published: January 2025

AbstractBackgroundOlder African American (AA) and Hispanic American (HA) adults have a higher risk for Alzheimer’s disease and related dementias (ADRD) compared to non‐Hispanic white (NHW) adults. AA and HA persons may also develop disease at younger ages with more rapid progression. Vascular disease, including cerebral small vessels, manifest by white matter hyperintensity (WMH) lesions, is more prevalent in AA and HA. Evidence suggests that vascular disease and WMH lesions may influence ADRD risk. This study explores WMH volume and vascular health conditions in a diverse research sample.MethodWe obtained demographic, clinical history, laboratory, and brain magnetic resonance imaging data collected from Health and Aging Brain Study: Health Disparities baseline visits. Using ANOVA and chi‐square, we compared conditions with ADRD risk (depression, hypertension, diabetes, hyperlipidemia, stroke), blood pressure, body mass index (BMI), and labs by self‐reported race (NHW, AA, HA). We performed linear regression adjusted for age, education, and sex to evaluate associations between WMH volume and clinical conditions.ResultData from 3,035 participants was analyzed (mean age: 65.2; female: 62.3%; impaired: 20.1%). AA were younger (NHW: 68.6, AA: 62.9, HA: 62.3. p<.0001) and had higher BMI (NHW: 29.1, AA: 32.5, HA: 30.9, p<.0001). Among conditions, HA had more hypertension (NHW: 18.2%, AA: 15.3%, HA: 19.9%, p<.0001), diabetes (NHW: 4.68%, AA: 4.71%, HA:12.4%, p<.0001), higher A1c (NHW: 5.57, AA: 6.02, HA: 6.43, p<.0001), and higher triglycerides (NHW:120, AA: 99.2, HA: 152, p<.0001). NHW had more depression (NHW:10.4%, AA: 4.88%, HA: 8.76%, p=0.006). AA had the highest WMH volume (NHW: 4.16, AA: 18.4, HA: 3.18, p<.0001). Regression showed higher WMH volume was associated with BMI in AA (β=.33, p=.007) and HA (β=1.84, p=.001). In AA, higher WMH volume was associated with diabetes (β=23.7, p=.02) and HbA1c (β=8.32, p=.02). In NHW, higher WMH volume was associated with stroke (β=4.76, p<.0001) and depression (β=1.18, p=.007).ConclusionStudy findings show associations between WMH burden and medical conditions, labs, and physical measures. Results also show that cerebral small vessel disease and cognitive impairment risk factors may vary by race. Study findings highlight the benefit of diverse research samples in gaining further insights into ADRD risk, pathogenesis, and disease progression.
Extracellular Vesicles for The Diagnosis of Alzheimer's Disease and Related Dementias: A Diagnostic Accuracy Meta‐analysis
Authors: Taha Hash Brown , Yacoub Fatemah , Morris Alanna , Birnbaum Allison 
Published: January 2025

AbstractBackgroundAlzheimer’s disease, along with other cognitive disorders such as frontotemporal dementia (FTD), dementia with Lewy body (DLB) Parkinson’s disease dementia (PDD), and vascular cognitive impairment and dementia (VCD), comprise a range of conditions with similar cognitive symptoms but different pathophysiology. Currently, there is no biomarkers to distinguish them before death. The pathophysiological mechanisms differ significantly across these disorders, which implies a varied response to potential treatments. Furthermore, MCI often acts as an early phase, frequently leading to dementia, especially AD. Here, we assessed the diagnostic accuracy of biomarkers in general extracellular vesicles (EVs) and speculative CNS‐enriched EVs using the receiver operating characteristic (ROC) models to discriminate each disorder from one another or from healthy controls (HCs).MethodWe conducted a PRISMA‐guided meta‐analysis on 49 studies to evaluate the diagnostic accuracy of biomarkers in general and speculative CNS‐enriched EVs for AD, FTD, DLB, PDD, VCD, MCI, and HCs, using hierarchical bivariate modeling of ROC statistics. Sub analyses were performed across EV types, mediums (e.g., CSF, plasma, serum, urine, saliva), isolation methods, among others. We also assessed publication bias and the potential number of missing studies with the trim‐and‐fill method and other robust statistical methods.ResultsThe analyses reveal that biomarkers derived from general EVs exhibit better diagnostic accuracy and less heterogeneity, variance, and publication bias than speculative CNS‐enriched EVs for all disease comparisons.ConclusionThese findings suggest that the usage of biomarkers derived from general EVs is superior to those from speculative CNS‐enriched EVs for the diagnosis of dementia.
VCAM is associated with impairment in individuals with amyloid and tau pathology
Authors: Vidor Pedro Rodrigues , Schlickmann Thomas Hugentobler , Machado Luiza Santos , da Rocha Andreia Silva , Ferrari‐Souza João Pedro , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) has been known for more than a century, but its complex pathophysiology remains unclear. Previous studies have been suggesting a potential role of neuroinflammation and cerebral vascular changes in AD progression. Part of the immune response relies on the role of Vascular Cell Adhesion Molecule (VCAM) in cell transit through the endothelium. However, there is little information about the impact of VCAM in AD‐related impairment. Here, we investigated the association of plasma VCAM levels with biological and clinical AD markers.MethodWe assessed 357 individuals from the Alzheimer's Disease Neuroimaging Initiative cohort with plasma VCAM and medical data available. Statistical analyses were performed using R Studio. Association with diagnosis was evaluated by a linear regression between VCAM and clinical diagnosis, with adjustments for age, sex, APOEε4 status and years of education. Regressions analysis was also used to assess the association of VCAM with Clinical Dementia Rating Scale ‐ Sum of Boxes (CDR‐SB), adjusting for age, sex, APOEε4 status and years of education, as well as cerebrospinal fluid Aβ42 and p‐Tau181.ResultThe group of individuals with dementia had higher blood VCAM levels (p=0.005) than both CN and MCI (p=0.003) groups, but there was no statistical significance between CN and MCI (Figure 1). VCAM showed a positive association (p = 0.018, Figure 2) with greater impairment as measured by CDR‐SB when adjustments for Aβ42 and p‐Tau181 were included.ConclusionOur results suggest differential effects of vascular factors in biological and clinical AD. Further studies are needed to assess whether these relations include causality and whether targeting VCAM can lead to improvements in neuroinflammatory or vascular‐related changes.
Relationship between cognitive impairment and hand grip strength in a population‐based cohort of older Nigerians: Data from the VALIANT Study
Authors: Akinyemi Rufus O. , Olalusi Oludotun V , Akinyemi Tolulope O , Ogunde Gabriel O , Yaria Joseph O , Cadmus Eniola O , Popoola Femi O , Akinyemi Joshua O. , Owolabi Mayowa O , Romero‐Ortuno Roman , Ogunniyi Adesola , Lawlor Brian 
Published: January 2025

AbstractBackgroundBy 2050, the prevalence of dementia is projected to triple with the greatest increases anticipated in Africa and Asia – largely attributable to population growth and cardiometabolic disorder. Hand‐grip strength (HGS) is a known predictor of cardiometabolic and cognitive health. The relationship between HGS and cognitive impairment (CI) among elderly West Africans is not known. We examined the relationship between HGS and cognitive impairment among older adults in a rural community in Ibadan, South West Nigeria.MethodThe Vascular heAlth, fraiLty and cognition in Ageing Nigerians (VALIANT) Study is an ongoing longitudinal community‐based cohort study aimed at exploring the association between cardiovascular health, cognition and frailty in Nigeria. One thousand participants have been recruited via a multistage, stratified cluster random sampling method recruited from a rural community in Ibadan and taken through a battery of cardiovascular, cognitive and frailty assessment tools. Data on HGS, obtained using a digital hand dynamometer, was available for 480 men and women aged≥50years. The Relationship between cognitive impairment (using MoCA <18) and HGS was examined using a multivariable adjusted logistic regression analysis. All associations were reported as ORs with the corresponding 95% confidence intervals (CI).ResultThe mean age of study participants was 64.5 (11.8) with predominant females (65%). Most participants (62.2%) were hypertensivewhile 22.9% were obese. The mean MOCA score in males and females were 22.155.7 and 17.82 6.6 respectively. Approximately 36% had moderate‐severe CI (MoCA<18), 43% had mild CI (MoCA 18‐25), 21% were normal (>25). There was a significant positive correlation between HGS and MoCA 0.45 (p <0.001). The independent determinants of moderate‐severe cognitive impairment in descending order of aOR (95%CI) were age 1.07 (1.03‐ 1.11), higher HGS 0.94 (0.90 ‐0.98), attainment of primary education 0.14 (0.07 – 0.29), secondary education 0.06 (0.02 – 0.16) and tertiary education 0.01 (0.01 – 0.14)ConclusionBesides older age and attainment of any form of education, higher HGS was independently associated with moderate‐severe cognitive impairment among elderly Nigerians. Further exploration of the link between HGS and early clinical/neuroimaging biomarkers of dementia is desirable.
Conversion to dementia up to the fourth annual follow‐up assessment and from a Brazilian cohort of older adults
Authors: Engelmann Gabriela Tomé Oliveira , Vieira Carolina Portugal , Burgos Ivonne Carolina Bolaños , Moro Giovanna Correia Pereira , Borges João Marcos Silva , Costa Julia Cardoso Costa Cardoso , Graciano Guilherme Fonseca , de Souza Aline Siqueira , Saldanha Marcelle Ferreira , Hansen Erika de Oliveira , Romano‐Silva Marco Aurélio , de Miranda Debora Marques , de Marco Luiz Armando Cunha , de Ávila Rafaela Teixeira , Viana Bernardo de Mattos , Bicalho Maria Aparecida Camargos 
Published: January 2025

AbstractBackgroundStudies comprising cohorts of cognitively unimpaired older adults or with mild cognitive impairment (MCI) that assesses clinical and biological characteristics to conversion to dementia are scarce in Brazil.MethodTo determine the median conversion time to dementia in a cohort of Brazilian older adults with low educational levels. In the cohort, participants undergo an annual comprehensive clinical and neuropsychological evaluation, complemented by neuroimaging and laboratory assessments to determine the diagnosis. Data from 225 older adults from the Cog‐Aging cohort was assessed and the first confirmed diagnosis was selected for this analysis: 67 Normal Cognition (NC) and 158 with MCI. Descriptive statistics were carried out according to normality tests. The time to conversion to dementia for participants with normal cognition (NC), Mild Cognitive Impairment (MCI) was assessed using the Kaplan‐Meier survival analysis. Only p<0.05 were considered. This study was approved by UFMG’s ethics committee.ResultThe median age at the beginning of the study was 73 years (IQR = 9.19) for NC group, and 77 years for MCI (IQR = 10.58). The median years for education was 4 years for both the NC (IQR = 4.50) and MCI (IQR = 2.00) groups. At the fourth annual follow‐up, 66 patients with NC remained stable, while 21 with MCI remitted to NC. The cumulative proportion surviving of MCI patients was 68,7% (SE .054) at 36 months, with 31 conversions to dementia and 37 remaining cases. At 36 months, no NC had converted to dementia. This occurred at 50 months. The median survival time was 42.74 months (SE 2.92; Lower Bound 37.01; Upper 48.48). In other terms, this means that 50% of MCI conversions to dementia occurrs up to 3.56 years. These differences were statistically significant with Log Rank (Mantel‐cox), Breslow (Generalized Wilcoxon) and Tarone‐Ware p≤ .001.ConclusionAt the fourth annual follow‐up assessment, patients with NC tend to remain stable, while 50% of participants with MCI convert to dementia up to 3.56 years. MCI poses a greater risk for conversion to dementia in the first’s three years of follow‐up in this sample, however more studies are still needed to assess other confounding variables.
Revolutionizing Dementia Care: Enhancing Talk Therapy with Fine‐Tuned Large Language Models Using GPT Self‐Generated Data
Authors: Xu Jia , Wei Tianyi , Hou Bojian , Demiris George , Shen Li 
Published: January 2025

AbstractBackgroundIt has been validated that dementia can be alleviated by talk therapy. While general large language models (LLMs) have the potential for talk therapy and dementia treatment, customized LLMs resembling a mental‐health therapist can be more professional and powerful. We introduced an LLM solution designed for tailored mental health support for future dementia care. By employing GPT self‐generated data, encompassing a diverse range of mental health scenarios, we've significantly enhanced the model’s ability to understand and engage with complex emotional and psychological queries from patients. This advancement in LLMs paves the way for more efficient and effective delivery of talk therapy, shedding light for future dementia care and treatment.MethodWe fine‐tuned five open‐source LLMs to enhance their capabilities in the context of mental health counseling. The fine‐tuning process was conducted using the quantized Low‐Rank Adapters (QLoRA) method on an A40 GPU. We generated 10,000 question‐answer pairs as the training data using GPT‐3.5‐Turbo, designed to simulate interactions between mental health counselors and patients. This dataset encompassed a broad spectrum of mental health issues, including but not limited to depression, anxiety, and sleep disorders. Post fine‐tuning, the performance of these models in test data gathered from the Mental Health Forum and Quora (Fig. 2) was judged comprehensively by GPT‐4 Turbo. This evaluation was structured around six mental health metrics (Fig. 1) to ensure precise and relevant assessment in the context of mental health therapy.ResultThe top‐performing model, “Mixtral‐8×7B‐v0.1‐qlora,” achieved a remarkable average score of 7.83 (Tab. 1), highlighting its superior capability over the base model in handling complex mental health interactions. Other fine‐tuned models, such as “Zephyr‐7b‐gpt” and “Vicuna‐7b‐gpt,” also showcased exceptional performance over their base models, scoring 7.82 and 7.78 respectively. These results underscore the efficacy of fine‐tuning in enhancing the empathetic and contextual responsiveness of LLMs in mental health settings, particularly in dementia care.ConclusionThe study’s results showcased the effectiveness of fine‐tuned large language models (LLMs) suffixed by “QLoRA” in enhancing talk therapy. The leading model, “Mistral‐7B‐Instruct‐v0.2‐QLoRA,” demonstrated superior empathy and understanding over the base model in complex dementia interactions by leveraging domain‐specific data.
A web‐based tool for early prediction of Alzheimer’s disease dementia based on magnetic resonance imaging data
Authors: Fan Yong 
Published: January 2025

AbstractBackgroundRecent studies have demonstrated that deep learning of magnetic resonance imaging (MRI) brain scans can accurately predict Alzheimer’s disease (AD) dementia and cognitive decline. However, the translational potential of this technique remains unfulfilled, as the underlying deep learning techniques are not yet available for immediate clinical use. To address this issue, we develop a web‐based tool to facilitate real‐time imaging data visualization and analyses, including brain image segmentation, cortical surface reconstruction, and early prediction of Alzheimer’s disease dementia based on structural MRI data.MethodA webapp has been developed to load and visualize imaging data with a browser‐based viewer and achieve fast imaging data processing and predictive modeling with a cloud‐computing backend. The browser‐based viewer provides a webpage‐based interface for communicating with the cloud‐computing backend and visualizing imaging data and analysis results. The cloud‐computing backend integrates state‐of‐the‐art deep learning algorithms to carry out real‐time brain image segmentation, cortical surface reconstruction, and early prediction of Alzheimer’s disease dementia based on structural MRI data. All the image processing and prediction results are automatically sent back to the browser‐based viewer for visualizing the results. This webapp supports imaging data in both DICOM and NIFTI formats and all identity information of imaging files are removed automatically before the imaging data being sent to the cloud‐computing backend for computing.ResultThis webapp is compatible across widely used web browsers, allowing real‐time image analysis without installing any software. It can load and visualize MRI images with drag‐and‐drop and achieve brain image segmentation, cortical surface reconstruction, and prediction of progression of Alzheimer’s disease dementia and cognitive decline within seconds. Validation results on large imaging datasets, including ADNI and AIBL, have shown that this tool can achieve fast and accurate prediction of Alzheimer’s disease dementia and cognitive decline.ConclusionThe webapp turns deep learning methods for MRI data analysis into a clinically usable tool, illustrating the feasibility of real‐time predictive modeling of Alzheimer’s disease dementia based on MRI data. Ongoing development will further increase its capacity to facilitate predictive modeling of diverse neurodegenerative diseases based on imaging data.
The Good Life program for healthy brain aging is a new standard of care in California
Authors: Johnson David K 
Published: January 2025

AbstractBackgroundIn 2022, The Good Life program for healthy brain aging (TGL) was recognized by Governor Newsom and his Task Force on Alzheimer’s Prevention and Preparedness as a new Standard of Care for Alzheimer’s disease preventive medicine in California. TGL is a large geriatric focused public health initiative that promotes healthy lifestyles to older adults from all walks of life. It is made up of a series of three lifestyle intervention (LSI) classes that are produced and delivered on a video conferencing platform. The Let’s Get Moving (LGM) series is a live, virtual exercise and fitness class delivered to seniors several times per week by a certified fitness trainer using the Exercise is Medicine curriculum of the American College of Sports Medicine. The Let’s Get Cooking (LGC) series delivers virtual healthy cooking classes to seniors in their own home by our staff trained in the 18 Reasons cooking curriculum, an empirically validated nutrition program that uses hands on kitchen training to promote caloric restriction and healthier meal replacement. The peer‐to‐peer mental health TGL program applies a group therapy‐like formatted virtual meeting series to promote mindfulness and emotional literacy.MethodTGL implemented a 2‐arm pragmatic clinical trial (without random assignment) to test if high versus low levels of Perceived Social Support, Perceived Positive Health Change, and Anxiety symptoms affect attendance in TGL’s intervention over an 18‐week repeated measures follow up. The study is in its 2nd year and will run over 4 years with quarterly assessments of the 3 co‐primary outcomes. Each participant is observed up to 18 months, yielding up to 6 repeated measures of these outcomes per individual.ResultTGL is very successful at engaging BA and LA in healthy lifestyle classes. TGL has over 2,000 older adult members (approximately 70% of our membership are either BA or LA older adults).ConclusionTGL’s success strongly indicates that virtual classes, which eliminates the burdens for older adults to attend in‐person classes, are effective in increasing adoption of healthy lifestyle behaviors.
Navigating the Complexities of Alzheimer’s Clinical Trials: The Perils of Small Molecule Studies and the Risk of Fraudulent Practices
Authors: O'Keefe Patrick P , Hendrix Suzanne B. , Zhang Jeffery , Hendrix Kent , Wang Lixia , Dickson Samuel P. 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) presents unique challenges in clinical trials involving small molecules. Multifaceted issues plague such trials, emphasizing susceptibility to fraud from clinical sites and “professional patients”. The relative ease of simulating Alzheimer’s diagnosis, coupled with inadequate oversight by Contract Research Organizations (CROs), creates fertile ground for deceptive practices. Poor rater quality and the simplicity of falsifying data exacerbate concerns.A core difficulty is the diagnostic ambiguity of AD, where symptomatic overlap with other cognitive disorders often leads to misdiagnosis or feigned conditions by professional patients. This issue is intensified in small molecule studies, which are easier to replicate or substitute than biologics, increasing the temptation and feasibility of fraudulent activities. Lax monitoring and control by CROs contribute to these vulnerabilities, allowing unnoticed data manipulation and deceitful conduct. It is particularly alarming that sites most prone to deceitful practices include predominantly underrepresented populations, thwarting inclusion efforts.MethodWe critically analyze the inefficacy of regulatory audits in detecting and preventing fraud as clinical sites adeptly prepare regulatory documents to mask misconduct. We evaluate methods for detecting fraudulent practices based on the clinical data and clinical operations metrics.ResultThe absence of medical expertise among auditors limits their ability to discern data integrity issues and the nuances of AD symptomatology. Traditional data management review and statistical analysis methods fail to identify these types of issues.ConclusionWe call for a reevaluation of current methodologies in AD clinical trials. We advocate for enhanced CRO oversight, improved rater training, and incorporation of medical expertise in audits. Targeted, blinded statistical data review can identify problematic sites. Addressing these challenges can safeguard AD research integrity, accelerate development of effective therapeutics, and protect and benefit vulnerable populations.