bluuebunny/crossref_metadata_2025_split huggingface dataset:
Blood DNA methylation biomarkers for cognitive decline in older adults with type 2 diabetes
Authors: Manzali Sigalit , Ravona‐Springer Ramit , Jacob‐Hirsch Jasmine , Heymann Anthony , Beeri Michal S , Greenbaum Lior 
Published: January 2025

AbstractBackgroundType 2 diabetes (T2D) is a recognized risk factor for dementia. This study aimed to pinpoint blood DNA methylation biomarkers for cognitive decline in older adults with T2D by comparing those who developed dementia with those who remained cognitively normal during follow‐upMethodIllumina Infinium MethylationEPIC microarray was used for the initial 24 couples and Infinium HumanMethylationEPIC microarray version 2.0 for the subsequent 8 couples. Beta and p‐values were calculated using Partek genomic suite and SeSAMe for the respective microarray versions. Human CpG annotation manifests for both versions were utilized, and quantile normalization (Partek genomic suite) facilitated comparisons across different arrays. Functional analysis employed Ingenuity IPA.ResultThe analysis covered 128 samples (32 converters and 32 matched non‐converters) at baseline and a 36‐month follow‐up. Methylation levels significantly differed between converters and non‐converters, with 950 sites altered in converters versus 25 in non‐converters (FDR corrected p value ≤0.05) during follow‐up. To identify potential biomarkers, methylation changes unique to baseline were examined. Of the 249,490 shared methylation sites, 526 were differentially methylated between converters and non‐converters at baseline (p‐value ≤0.05, fold change ≥1.5). Notably, 293 sites were altered solely at baseline, while 233 showed differential methylation at both baseline and 36‐months follow‐up. Among the 169 genes related to the 293 sites altered only at baseline, Ingenuity IPA analysis identified 29 genes linked to neurodegeneration and cognitive impairment (17%) and 38 genes associated with inflammation (22%). Another set of interest included 137 genes related to the 233 sites differentiating converters from non‐converters at both time points, with some genes implicated in neurotransmitter secretion and regulation of neuron differentiation.ConclusionThese initial findings indicate significant blood methylation changes early in cognitive decline among older adults with T2D. These alterations, if validated in additional cohorts, could serve as biomarkers for T2D‐related cognitive decline.
Plasma amyloid‐β40/42 ratio can distinguish co‐morbid Alzheimer’s disease pathology in patients with Lewy body disease
Authors: Shinohara Moeko , Murakami Hidetomo , Sakashita Yasuhiro , Mori Yukiko , Komatsu Junji , Muramatsu Daiki , Hikishima Sadao , Ono Kenjiro 
Published: January 2025

AbstractBackgroundAlthough comorbidity with Alzheimer’s disease (AD) is not uncommon in Lewy body disease (LBD), no blood biomarkers which can identify AD pathology in LBD have ever been established.ObjectivesTo investigate if plasma amyloid‐β (Aβ) biomarkers can distinguish AD comorbidity in LBD patients.MethodsWe examined plasma Aβ40, and Aβ42 in 15 patients with Aβ negative LBD, 10 with Aβ positive LBD, and 37 with AD. In addition, patients underwent amyloid‐positron emission tomography imaging (11C Pittsburgh compound‐B) or cerebrospinal fluid Aβ42 test.ResultsThe plasma Aβ40/Aβ42 ratio was significantly decreased in patients with Aβ+ LBD compared with those with Aβ‐ LBD (p = 0.037) and could accurately classify the two diseases (area under the curve, 0.727).ConclusionsPlasma Aβ40/Aβ42 ratio might be a useful marker for comorbid AD pathology in LBD.
RWD insights into the use, safety and effectiveness of novel treatments
Authors: Iwata Atsushi 
Published: January 2025

AbstractIn Japan, the regulatory authority approved the drug in September 2023, and on December 20, it became available for prescription country‐wide under the health insurance system. However, there are strict patient, physician, and facility requirements for the prescription of Lecanemab, and various problems are anticipated in its future implementation and widespread use in society.Lecanemab is the first anti‐Aβ antibody in Japan, and even dementia specialists do not have sufficient knowledge and experience in its introduction, evaluation of efficacy, and evaluation and handling of side effects. In particular, it is important to establish a smooth flow of referrals from primary care physicians to specialized medical institutions and the sharing of correct awareness of the drug in order to expand the range of this drug’s use.In addition, the Japanese government has instructed Eisai to conduct a post‐marketing surveillance of all patients in order to properly evaluate the efficacy and adverse effects of the drug. Therefore, various data will be obtained within this framework, but since some ADL scales and raw imaging data will not be collected, a registry is planned to be started by academia to supplement Eisai’s database. As a pilot study, we have established an electronic data collection system (EDC) and a mechanism for registering data to an imaging database, and named it REGI‐ALZ (Real‐world Evidence Generating Initiative for ALZheimer’s isease). In Japan, APOE genetic testing can only be done for research purposes because there is no approved in vitro diagnostic test for APOE. Therefore, RFEGI‐ALZ was designed to allow data collection for the purpose of measuring APOE. We hope that this system will serve as a reference for the registry of academia that will be established in the future.
SomaScan plasma proteomics of cSVD
Authors: Gomez Gabriela T. , Shi Liu , Fohner Alison E , Chen Jingsha , Yang Yunju , Fornage Myriam , Duggan Michael R , Peng Zhongsheng , Daya Gulzar , Tin Adrienne , Schlosser Pascal , Longstreth W.T. , Kalani Rizwan , Sharma Malveeka , Psaty Bruce M. , Nevado‐Holgado Alejo J , Buckley Noel , Gottesman Rebecca F. , Lutsey Pamela L. , Jack Clifford R. , Sullivan Kevin J , Mosley Thomas H. , Hughes Timothy M. , Coresh Josef , Walker Keenan A. , Coresh Josef 
Published: January 2025

AbstractBackgroundCerebral small vessel disease (cSVD), as defined by neuroimaging characteristics such as white matter hyperintensities (WMHs), cerebral microhemorrhages (CMHs), and lacunar infarcts, is highly prevalent and has been associated with dementia risk and other clinical sequelae. Although risk factors for cSVD have been identified, little is known about the biological processes and molecular mediators that influence cSVD development and progression.MethodsWithin the Atherosclerosis Risk in Communities (ARIC) study, we used SomaScan Multiplexed Proteomic technology to relate 4,877 plasma proteins to concurrently measured MRI‐defined cSVD characteristics, including WMHs, CMHs, and lacunar infarcts, in late‐life (n=1508; mean age: 76). Candidate protein associations with WMH volume were replicated in the Cardiovascular Health Study (n=765; mean age: 73) and Baltimore Longitudinal Study of Aging (n=902; mean age: 66). Cohort‐specific effect estimates were pooled using a fixed‐effect, inverse variance weighted meta‐analysis. Within 4228 non‐demented older adults in ARIC, we examined the association of WMH‐associated proteins with eight‐year dementia risk (641 incident dementia cases) using multivariate Cox proportional hazard models. All multivariate regression models were adjusted for demographic, kidney function, and cardiovascular risk variables.ResultsThis proteome‐wide analysis of older adults identified 13 WMH‐associated plasma proteins involved in synaptic function, endothelial integrity, and angiogenesis (Figures 1a, 2a). Nine of these proteins replicated in one or more external cohort (Figures 1b, 2b) and two proteins, oligodendrocyte myelin glycoprotein (OMG) and neuronal pentraxin receptor (NPTXR), remained associated with late‐life WMH volume when measured nearly two decades earlier during midlife (Figures 1c, 2c). Eight of the 13 WMH‐associated proteins were associated with lacunar infarcts, 6 with subcortical CMHs, and 4 with lobar CMHs (Figures 1d, 2d). Although none of the genes coding for WMH‐associated proteins have been nominated by WMH GWAS, 7 of these genes have been previously linked to cardiometabolic traits known to influence cSVD, such as blood pressure and cholesterol (Figures 1e, 2e). Finally, 11 candidate proteins were associated with incident dementia, underscoring their clinical relevance (Figures 1f, 2f).ConclusionsThis large‐scale proteomic analysis within longitudinal community‐based samples identified several plasma proteins, including OMG and NPTXR, as top candidate biomarkers for elevated WMH volume and its clinical manifestations.
Differences in protein expression in Alzheimer’s disease patients based on risk factors for amyloid‐related imaging abnormalities
Authors: Oosthoek Marlies , Vermunt Lisa , Blujdea Elena Raluca , in 't Veld Sjors G.J.G. , van der Flier Wiesje M. , Barkhof Frederik , del Campo Marta , Avilés Martín Pucheu , Visser Allerdien , Schut Martijn C. , Vijverberg Everard G.B. , Teunissen Charlotte 
Published: January 2025

AbstractBackgroundThe exact mechanism underlying amyloid‐related imaging abnormalities (ARIA) is unknown. Several factors explain ARIA risk, including the presence of microbleeds, APOE4 carriership, and very low Aβ42 levels. The cerebrospinal fluid (CSF) proteome reflects ongoing mechanisms and, thereby, provides an accessible fluid to refine risk of ARIA development. In this explorative study, we aimed to determine how the CSF proteome relates to the ARIA risk profile and identify potential biomarkers for ARIA risk.MethodWe included AD dementia patients who were Aβ42/tTau ratio positive (n=156; median age: 67 IQR:60‐72, 38% F, median MMSE‐score: 19.8 IQR:17‐24). We dichotomized all risk factors: microbleeds were divided in 0 (M‐, n=123) or >0 (M+, n=33), APOE4 status was divided in carriers (APOE4+, n=96) and non‐carriers (APOE4‐, n=60), CSF Aβ42 levels were separated based on the median (Low and High). We also defined a high and low risk profile: patients with M+APOE4+AL (n=13) and M‐APOE4‐AH (n=23). CSF proteomics were performed using antibody‐based proteomics (Olink), and we selected 288 proteins previously shown to be related to AD (del Campo et al., 2022). With linear regression analysis (with age and sex corrections), we compared protein levels per risk group.ResultWe identified 20 proteins that were differentially expressed in M+ versus M‐, 7 proteins in APOE4+ versus APOE4‐, 3 proteins in Aβ42L versus Aβ42H, and 49 proteins in M+APOE4+AL to M‐APOE4‐AH. The top proteins include EGFR, CRTAC1, AXL, CNTN1, and TIMP4. Overall, almost all proteins were downregulated in the ARIA risk group and most proteins were not overlapping between risk groups.ConclusionThese results show that there are differences based on the ARIA risk profile. The differentiating proteins could provide extra information on the ongoing biological processes that increase ARIA risk and in the future aid to refine ARIA risk. Potentially, these results could aid in the development of a new biomarker for ARIA risk. Future analysis focus on further understanding the proteins involved and validating the findings.
Use of Design Thinking to Develop Approaches to Collect and Use Person‐Centered Dementia Care Measures in Low‐Resource Long‐Term Care Settings
Authors: Corazzini Kirsten , Lepore Michael , Kusmaul Nancy , Wang Jing , Rataj Alison , Kim Yoon Chung , Murray Briana , Davie Laura , McConnell Eleanor S , Holmes Sarah D 
Published: January 2025

AbstractBackgroundLow‐resource residential long‐term care (LTC) settings, including settings located in medically underserved and health professional shortage communities, have fewer environmental resources to support high‐quality, robust data collection and use of measures to support person‐centered dementia care (PCC). Further, such settings are more likely to serve older adults from populations that have experienced historic harms related to misuse of personal data, including low‐income and minoritized populations. Design thinking engages community‐members to understand a problem from the end‐user’s perspective (empathize and define), brainstorm new solutions (ideate), and develop proposed solutions (prototype and test). This study describes the development of user‐design workshops to co‐develop, with residents living with dementia (RLWD), their family members and care staff, a person‐centered measurement collection and data sharing infrastructure in low‐resource residential dementia care settings.MethodsThe overall approach is a multi‐method, community‐based, participatory research study with design thinking principles. Stakeholders in each of four low‐resource LTC settings provided in depth perspectives on collecting and sharing data about RLWD to support PCC. Results from qualitative thematic analysis informed the development of user‐design workshops to co‐design feasible and acceptable data collection and use protocols.ResultsResidents, relatives, and staff community members (N = 68 participants) were recruited from 4 low‐resource residential LTC settings (N = 2 rural, 2 urban settings). Journey maps were developed to capture community‐member perspectives by drawing upon themes in each of four key areas: identifying information for care; accessing information for care; sharing information for care; and prioritizing information for care. Journey maps provided the structure to develop co‐design workshop protocols to re‐engage community‐members to empathize, define, ideate, and prototype acceptable and feasible data collection and data sharing protocols of PCC measures.ConclusionFindings from this study capture multiple facilitators and barriers to robust data collection and use protocols in resource‐constrained LTC environments. However, findings also demonstrate the potential for user‐centered design approaches to generate novel solutions in ways that honor the perspectives of RLWD, their family members, and the staff who care for them. Ultimately, such approaches contribute to the growth of inclusive research infrastructure to grow capacity for high quality, person‐centered dementia care.
Classification of Patients with Dementia with Lewy Bodies by Event‐related Oscillations
Authors: Güntekin Bahar , Duygun Rümeysa , Bölükbaş Burcu , Kuloğlu Hikmet Buse , Yemeniciler İrem , Yirikogullari Harun , Helvacı‐Yılmaz Nesrin , Hanoğlu Lutfu 
Published: January 2025

AbstractBackgroundIn the EEG research of Alzheimer's Disease (AD) and other diseases belonging to dementia, the literature is rapidly growing to indicate biomarkers specific to the type of dementia. The literature showed firm conclusions that decreasing event‐related delta and theta responses could be a biomarker showing cognitive decline (Güntekin et al. 2022). However, event‐related EEG biomarkers of Lewy Body with dementia (DLB) were not studied well. The present study aims to differentiate the event‐related oscillations between DLB patients with healthy controls during cognitive paradigms.Method19 DLB patients and aged‐matched 20 healthy controls (HC) were included in the study. 32‐channel EEG was recorded during the visual oddball paradigm. Event‐related power spectrum and phase‐locking were analyzed for the delta (0,5‐3,5 Hz), theta (4‐7 Hz), alpha (8‐13 Hz), beta (15‐28 Hz), and gamma (28‐48 Hz) frequency bands. The statistical analyses were run with repeated measures of ANOVA.ResultDLB patients had decreased event‐related delta and theta power and phase‐locking compared to HC (p<0.001; p<0.001). There is no differentiation in event‐related alpha, beta, and gamma power between DLB patients and HC controls. The phase locking factor could differentiate better between healthy controls and DLB patients in the higher frequency bands. Event‐related alpha (p=0.002), beta(p=0.001), and gamma (p=0.010) phase locking of DLB patients were decreased in DLB patients compared to HC.ConclusionThe present study showed that, like other types of dementia, DLB patients had reduced delta and theta power and phase locking compared to HC. The event‐related phase locking factor in all frequency bands was more effective in differentiating DLB than HC and could be used a biomarker. Furthermore, the phase locking factor in delta, theta, alpha, beta, and gamma frequency bands could be used with machine learning approaches to classify HC and DLB.
The Impact of Diet on AD Biomarkers in a Community‐Based Sample
Authors: Okafor Amarachukwu , Vartak Ambika , Johnson Aubrey S. , Guzmán Diana S. , Smith Anna C. , Houlihan Hannah M. , Heuer Lauren B. , Rentería Miguel Arce , Turney Indira C. , Mayeux Richard , Brickman Adam M. , Manly Jennifer J. , Gu Yian , Lao Patrick J. 
Published: January 2025

AbstractBackgroundGreater adherence to the Mediterranean Diet (MeDi) is associated with lower risk for cardiovascular disease, slower cognitive decline, and reduced risk for Alzheimer’s Disease (AD). However, its association with AD biomarkers is not well known. We hypothesized that greater MeDi adherence is associated with reduced amyloid and tau PET burden in a community‐based sample of older adults in Northern Manhattan.MethodsParticipants from the Washington Heights/Inwood Columbia Aging Project (WHICAP) completed the Willet Food Frequency questionnaire (FFQ) at study baseline. A subset completed one (n=161 participants; age=81±6 years, 20% Hispanic, 50% Black, 65% women, education=13±4 years) or two timepoints of amyloid PET (FBB, n=19; follow‐up time=6.4±1.9), as well as one timepoint of tau PET (MK‐6240, n=17). PET scans were 8.6±4.6 years after the FFQ. We evaluated the impact of MeDi adherence on subsequent baseline amyloid and tau burden, quantified as SUVR or positivity (SUVR>1.25 for amyloid or SUVR>1.81 for tau), as well as the annualized percent change in amyloid burden over time, accounting for age, sex, race and ethnicity, education, caloric intake, and time between the FFQ and PET scan.ResultsFifty‐eight (36%) participants were amyloid positive and 7 (41%) were tau positive in early Braak regions at their first timepoint, and 2 (8%) individuals converted from amyloid negative to amyloid positive at their second timepoint. Greater MeDi adherence was not associated with amyloid burden (‐0.02 [‐0.04, 0.004], p=0.11) but was marginally associated with a lower likelihood of amyloid positivity (‐0.21 [‐0.44, 0.0009], p=0.055). Greater MeDi adherence was not associated with tau burden (0.003 [‐0.13, 0.13], p=0.97) or the likelihood of tau positivity (‐0.66 [‐1.9, 0.63], p=0.3). MeDi adherence was not associated with the change in amyloid burden over time (‐0.19 [‐1.5, 1.1], p=0.8).ConclusionGreater MeDi adherence was associated with a reduced likelihood of subsequent amyloid positivity, suggesting diet may play a role in amyloid‐related AD risk. Replication in larger studies is needed. Further attention should be placed on the individual components of the MeDi adherence score (e.g. fish, vegetables) and their differential consumption and effects across racial and ethnic groups.
Evaluating brain age using functional connectivity in autosomal dominant Alzheimer disease
Authors: Macharia Kiana Angela , Gordon Brian A. , Wisch Julie K. , Metcalf Nicholas V , Roman June , Benzinger Tammie L.S. , Cruchaga Carlos , Goate Alison M. , Hassenstab Jason J. , Ibanez Laura , Karch Celeste M. , Llibre‐Guerra Jorge J. , Morris John C. , Perrin Richard J. , Renton Alan E. , Supnet Charlene B , Xiong Chengjie ,   Dominantly Inherited Alzheimer Network (DIAN), Bateman Randall J. , McDade Eric , Ances Beau , Millar Peter R 
Published: January 2025

AbstractBackgroundBrain‐predicted age estimates are used to quantify an individual's brain age compared to a normative trajectory. We have recently shown that brain age from structural MRI is elevated in autosomal dominant Alzheimer disease (ADAD), a unique sample that allows the study of AD progression independently of age‐related confounds. Resting‐state functional connectivity (FC) may capture a biphasic response to sporadic AD, and thus may complement structural measures of brain aging in ADAD.MethodWe trained a Gaussian Process Regression (GPR) model to predict age from FC in 271 regions of interest in 779 cognitively unimpaired, amyloid‐negative, non‐ADAD participants. We applied the model in a cohort of 256 ADAD mutation carriers (MCs) and 200 familial non‐carrier controls (NCs) from the Dominantly Inherited Alzheimer Network (DIAN). We then assessed whether the brain age gap (FC‐BAG; difference between predicted and actual age) varied based on mutation status, cognitive status, or estimated years until symptom onset (EYO). We also explored its associations with markers of amyloid (PiB PET, CSF amyloid‐β‐42/40), phosphorylated tau (CSF and plasma pTau‐181), neurodegeneration (CSF and plasma neurofilament light (NfL)), and cognition (global neuropsychological composite and CDR®‐sum of boxes).ResultThe model accurately predicted age in the training set (r = 0.77), validation set (r = 0.76), and DIAN NCs (r = 0.82), Figure 1. FC‐BAG was elevated in symptomatic MCs (p < .001), but only marginal elevation was observed in asymptomatic MCs (p = 0.047), Figure 2. In symptomatic MCs, FC‐BAG weakly associated with pTau‐181 in CSF (r = 0.34, p = 0.0046) and plasma (r = 0.26, p = 0.02), but no other associations with AD biomarkers or cognition were observed, Figure 3.ConclusionFC‐BAG was elevated in symptomatic ADAD, consistent with recent demonstrations in sporadic AD. However, we did not detect a predicted biphasic response to presymptomatic ADAD pathology. This inconsistency may be driven by differences between AD forms or by low reliability of FC, which may result in low statistical power and inconsistent brain‐behavior relationships. FC‐based BAG offers unique benefits but ultimately does not function as a reliable model for observing advanced aging in preclinical ADAD.