bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations of plasma biomarkers of neurodegeneration with characteristics related to type 1 diabetes
Authors: Delgado Nikki , Chaytor Naomi Sage , Germine Laura Thi , Hawks Zoë W , Jung Laneé , Cleveland Michael , Bulger Jane , Grinspoon Elizabeth , Janess Kamille , Sliwinski Martin J. , Weinstock Ruth S , Chhatwal Jasmeer P. , Kivisäkk Pia , Beeri Michal S , Fonseca Luciana Mascarenhas 
Published: January 2025

AbstractBackgroundCognitive dysfunction is more common in individuals with type 1 diabetes (T1D) compared to the general population. Blood‐based biomarkers are accurate in identifying early signs of neurodegeneration. However, studies using these biomarkers in T1D are lacking. We examined the associations of plasma biomarkers and diabetes characteristics in adults with T1D.MethodsThis study included 114 adults with T1D from the Glycemic Variability and Fluctuations in Cognitive Status in Adults With Type 1 Diabetes (GluCog) study. Plasma biomarkers were: ratio of amyloid‐β (Aβ) 42/40, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated Tau181 (pTau181) using Quanterix assays, and phosphorylated Tau 217 (pTau217) using ALZpath. The coefficient of variation (%) from continuous glucose monitor readings was used to define glycemic variability (GV). Relationships between biomarkers, demographics, and diabetes characteristics were evaluated using Spearman rank correlation, Mann‐Whitney U, and Kruskal‐Wallis tests, as appropriate. Linear regression models were used to examine the associations between plasma biomarkers (dependent variable) and diabetes characteristics (that were significant in bivariate analyses) as predictors in separate models adjusted for sex, age, and education. We added the adjustment for nephropathy in models in which the biomarker was associated with this variable.ResultsThe sample [53% female, mean(SD) age of T1D diagnosis 20.3(12.7) years, 37% with hypertension, 11% with neuropathy, 8% with nephropathy)] had a baseline age of 49.0(14.5) years, HbA1c 7.6%(1.3), GV 36.6%(7.3). Adjusted linear regression models revealed significant associations with four biomarkers. Presence of nephropathy was associated with higher concentrations of NfL, pTau181, and pTau217 and was included in the models with these biomarkers. A lower Aβ42/40 ratio was associated with older age at diagnosis. A higher concentration of NfL was associated with higher HbA1c. A higher concentration of pTau181 was associated with higher GV. Lastly, higher pTau217 was associated with neuropathy. GFAP was not associated with any of the diabetes measures.ConclusionThese data show that plasma biomarkers of neurodegeneration are associated with key diabetes characteristics in adults with T1D, particularly HbA1c, GV, neuropathy, and nephropathy. Further research is needed to understand whether T1D characteristics mediate biomarker associations with cognitive decline.
A polymorphism in RELN protects against amyloid‐driven tau pathology and cognitive decline in sporadic Alzheimer's disease
Authors: Carello‐Collar Giovanna , Schlickmann Thomas Hugentobler , Ferrari‐Souza João Pedro , de Bastiani Marco Antônio , Cristino Alexandre Santos , Pascoal Tharick A. , Rosa‐Neto Pedro , Souza Diogo O. , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundA rare reelin gene variant (RELN‐COLBOS mutation) delayed dementia onset in almost 30 years in an autosomal dominant Alzheimer’s disease (ADAD) carrier. This patient presented with high amyloid‐β (Aβ) plaque load, but low tau accumulation, suggesting that this single‐nucleotide polymorphism (SNP) in RELN conferred a resilience not only to cognitive decline but also to tauopathy in ADAD. However, whether RELN SNPs are also protective in sporadic Alzheimer’s disease (AD) is yet to be determined. Thus, we sought to examine the impact of RELN SNPs on AD pathophysiology and cognitive deterioration in sporadic AD.MethodWe assessed 198 individuals [105 cognitively unimpaired (CU) and 85 cognitively impaired (CI)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with available data on RELN SNPs and amyloid‐ and tau‐PET measures ([18F]‐florbetaben/florbetapir and [18F]‐flortaucipir, respectively), Aβ1‐42 and ptau181 in the CSF, and neuropsychological testing (Clinical Dementia Rating Sum of Boxes). We analyzed the effect of RELN SNPs carriership in the association between amyloid and tau burden through linear regression analysis and on cognitive decline according to CSF AT status through linear mixed‐effect model correcting for age, sex, and ApoEε4 status (Bonferroni’s adjusted p‐value < 0.05).ResultWe performed linear regression analysis in all the 235 RELN SNPs available on ADNI (Figure 1). We found RELN rs802787 protected against amyloid‐driven tau pathology (adj. p‐value < 0.001, Figure 2). Dividing individuals according to the CSF AT status, we observed that RELN rs802787 did not impact the rate of decline in cognition in A‐T‐ and A‐T+ individuals (Figure 3A‐B). By contrast, RELN rs802787 CSF A+T‐ individuals presented a slower cognitive decline (Figure 3C), which was not observed in A+T+ individuals (Figure 3D).ConclusionHere, we show RELN rs802787 carriers presenting high amyloid load have lower tau accumulation than non‐carriers. In addition, CSF A+T‐ RELN carriers presented a slower cognitive decline compared to non‐carriers. Our results suggest that RELN rs802787 carriership protects against amyloid‐driven tau pathology and cognitive deterioration in sporadic AD individuals. To the best of our knowledge, this is the first RELN SNP found to be protective against non‐familial AD.
Alpha‐internexin is detectable in cerebrospinal fluid – an analogue to neurofilament light?
Authors: Meda Francisco J , Johansson Camilla , Palm Karin , Brinkmalm Gunnar , Andreasson Ulf , Blennow Kaj , Kvartsberg Hlin , Zetterberg Henrik 
Published: January 2025

AbstractBackgroundAlpha‐internexin (AINX) is a type IV intermediate filament alongside the neurofilament triplet proteins. Although AINX is mostly expressed during the development of the brain, it is still present in mature neurons and reported to be involved in axonal outgrowth. Because of a lack of previously published studies quantifying AINX in cerebrospinal fluid (CSF), and its high sequence similarity with other neurofilaments, the objective of this project was to specifically detect AINX via antibody‐based techniques and assess its biomarker capabilities.MethodAINX in‐house antibodies (referred to as B15 and Ina1), alongside commercial antibodies (PA5‐95368 and MA5‐32809) were used to detect AINX in CSF and brain using ELISA and Simoa platforms. A recombinant AINX protein (AINX 89‐499) produced in‐house was used as positive control and calibrator. All antibodies were conjugated on beads, coated on microtiter plates, or biotinylated following standard protocols for usage on both platforms. Immunoprecipitation followed by mass spectrometry (IP‐MS), was also performed to evaluate antibody binding and specificity.ResultIn ELISA, all antibody combinations (capture/detector) were able to detect recombinant protein, with B15, Ina1 and PA5‐95368 providing the most effective combinations. While we were able to detect AINX in SDS brain extracts, only a few CSF samples generated any signal using ELISA. The low signals in CSF indicated that the sensitivity of ELISA might be a limiting factor. Therefore, we moved to the ultrasensitive Simoa platform. Here, we were able to show that AINX is detectable in CSF at low concentrations (1‐10pg/mL), with its increase correlating with CSF neurofilament light levels. IP‐MS results on SDS brain extracts revealed high sequence coverage of AINX for all three antibodies (90% for PA5‐95368, 56% for B15 and 85% for Ina1) thus confirming antibody specificity.ConclusionAll antibodies tested seem to be very suitable for immunoassay development and AINX is present in CSF at low abundance, with the Simoa platform being able to detect it due to its high sensitivity. Future work will focus on evaluating the biomarker potential of AINX in different neurodegenerative diseases and acute conditions such as stroke or traumatic brain injury.
Qypredict®: A promising prognostic tool for anti‐amyloid therapy patient prioritization
Authors: Villa Luca , Jubault Thomas , Guizard Nicolas , Gordon Elizabeth 
Published: January 2025

AbstractBackgroundThe approval of anti‐amyloid therapies has led to a breakthrough in the treatment of Alzheimer’s disease. However, given these treatments remain expensive and are associated with significant side effects, identifying patients that would, and importantly would not, benefit from treatment is crucial in optimizing costs and minimizing health risks. QyPredict®, an advanced machine learning‐based predictive platform, has the potential to identify patients most likely to show cognitive decline, and may prove beneficial at prioritizing patient selection for therapies. Given limited healthcare resources, predictive tools need to perform well using only the most accessible and widely available clinical and demographic data and be trained on a broad and generalizable population.MethodUsing data from ADNI, OASIS, and NACC, QyPredict® was applied to 1784 older individuals. To represent all individuals that may require future cognitive assessment, a variety of patient groups were included. The sample (mean and standard deviation (SD) age: 73.3(7.0), 42.2% female; baseline MMSE: 27.5(2.5)) was comprised of healthy controls (n=512), Alzheimer’s Disease patients (n=382), Mild Cognitive Impairment patients (n=660), and a mixture of other dementias, Parkinson’s disease, and psychiatric conditions (n=230). Cognitive decline was defined as an increase of at least 0.5 points on the CDR‐SB over 12 months. Model features included baseline MMSE, baseline CDR‐SB, sex, and regional grey matter volumes automatically derived using QyScore®, an FDA‐cleared and CE‐marked advanced neuroimaging platform.ResultPredicted decliners showed 5.8x greater cognitive decline on the CDR‐SB over 12 months compared to individuals predicted at baseline to remain stable (Predicted‐Decliner: 1.0(1.8) vs Predicted‐Stable: 0.17(0.85): Figure 1), which was statistically significant (t‐test: t(1782)=31.2, p<0.001). QyPredict® performed well, achieving the following: specificity: 0.81; sensitivity: 0.56; balanced accuracy: 0.69; negative predictive value: 0.76; ROC AUC: 0.77; positive predictive value: 0.64; F1 score: 0.6; precision: 0.62 (Figure 2). Baseline CDR‐SB, MMSE and QyScore®’s MRI markers of hippocampal and insular volumes demonstrated the greatest predictive value (Figure 3.)ConclusionUsing minimal clinical and demographic data, QyPredict® was able to accurately predict cognitive decline over a 12‐month period – regardless of diagnostic status. This may be invaluable when trying to prioritize which patients should receive anti‐amyloid therapies.
Head‐to‐Head Comparison of Plasma vs. Saliva Extracellular Vesicle Subpopulations Derived Normal Controls and Cognitively Impaired Subjects
Authors: Winston Charisse N. , Sarsoza Floyd 
Published: January 2025

AbstractBackgroundThe ability to quantify pathogenic proteins related to Alzheimer’s disease (AD) in plasma neuronal extracellular vesicles (NEVs) was vital to their development as diagnostic biomarkers. A similar approach can be employed to assess the biomarker potential of saliva EVs. A head‐to‐head comparison was conducted to characterize plasma and saliva EVs derived patients enrolled in the Nathan Shock Healthy Aging Study, where participants range in age from 30–70+ years, representing the full breadth of the healthy adult human age span.MethodMatched plasma and saliva EVs were precipitated from two cohorts, normal controls (NC, 55+ years of age) and patients with mild cognitive impairment (MCI; 55+ years of age) as defined by the Modified Mini‐Mental State Test (3MS). EVs were precipitated and enriched against neuronal‐origin, L1CAM using magnetic immunocapture and fluorescence‐activated cell sorting. EVs and NEVs were characterized for size and shape using NanoSight and marker profiling using Western Blot. AD‐related pathogenic proteins Aβ40–42, p‐tau, and Nf‐L will be quantified in EVs using SIMOA assays.ResultPlasma and saliva EVs derived from NC and MCI subjects demonstrated similar size distributions and shape characteristics as previously reported. EV marker profiling revealed differential expression of well characterized EV and cellular markers in saliva and plasma EVs. We determined that the rabbit monoclonal antibody raised against tetraspannin CD81 (Abcam) did not identify populations of plasma and saliva EVs while mouse monoclonal antibody against CD81 (Santa Cruz) identified separate populations of plasma and saliva EVs. CD9, TSG101 and ALIX markers positively identified subpopulations of EVs in plasma and saliva while CD63+ EVs were only observed in plasma EVs derived from NC and MCI subjects. MAP2 confirmed neuronal origin of NEVs derived from the plasma of NC and MCI subjects.ConclusionThe presence of different subpopulations of EVs in plasma and saliva may impact their AD biomarker potential and requires further investigation. Additional analyses will be employed to confirm neuronal origin of EVs from saliva. EV cargo analyses will be performed to assess the biomarker potential of plasma and saliva EVs derived from NC and MCI subjects.
Validation study of the effectiveness of a stepwise care program for dementia family caregivers
Authors: Suh Jeewon , Kang Mi Kyoung , Lee Kyung‐hae , Park Myoung Hwa , Koh Im‐Seok 
Published: January 2025

AbstractObjectiveThis study aims to validate the effectiveness of the education program for dementia family caregivers (so‐called Value Care program), and to assess the applicability of the program at local dementia centers in the community.MethodThe Value Care program is an educational program designed for family caregivers of dementia, conducted once a week for 90 minutes over a total duration of 8 weeks. This program aims to enhance understanding of the characteristics and symptoms of dementia at various stages and to provide specific response strategies for caregivers.In this case‐control study, family caregivers of dementia patients were recruited from 38 local dementia centers in Korea. Each participants were randomly assigned to either the experimental group or the control group in a 1:1 ratio. After 8 weeks, assessment was conducted on measure such as depression, care‐burden scales, caregiver self‐efficacy and satisfaction surveys.ResultsA total of 226 family caregivers were recruited in the experimental group, and 210 participants in the control group. The experimental group showed significant reductions in depression (P<0.001) and care‐burden scores (P<0.001) and significant increases in caregiver self‐efficacy scores (P<0.001) after participating in the Value Care program. The overall satisfaction score averaged 4.47 over 5.ConclusionThe “Value Care Program” demonstrated effectiveness in reducing depression, care burden, and enhancing the efficacy of care for family caregivers of dementia patients. Participants expressed a high likelihood of rejoining the program and recommending it to others. Therefore, the Value Care Program could be a crucial initiative for alleviating caregiver burden, stress, and feelings of depression among dementia family caregivers.
Dissociable effect of different measures of Vascular Metrics on Cognitive Function: The Mediating role of the Thalamic Subregions
Authors: Konwar Srijan , Bocchetta Martina , Marco Matteo De , Venneri Annalena 
Published: January 2025

AbstractBackgroundThe impact of white matter hyperintensities (WMH) on cognitive performance is uncertain due to inconsistent findings. The thalamus is of particular interest given its susceptibility to vascular damage. To test how different vascular risks (Deep/Periventricular WMH and Vascular Scores (VS)) affect thalamic subregional volumes and whether that has any mediating effect on different cognitive domains in healthy controls, mild cognitive impairment (MCI) and AD.MethodA sub‐sample of 93 participants (age = 64.96 ± 10.847, sex = 49 males/44 females) included controls (n = 40), MCI (n = 28) and AD (n = 25) were selected from the larger multi‐site VPH‐DARE cohort. 3T1‐weighted MRI images were pre‐processed using Freesurfer and its module to segment into 6 thalamic regions and its subcomponents. MD values of the bilateral posterior thalamic radiation (PTR) were extracted. Lexical semantic, episodic and executive functions were assessed. A non‐parametric mediation analysis was run using a bias‐corrected percentile bootstrapped with 5000 permutations controlling for covariates. A complementary analysis was conducted using the mean diffusivity (MD) values of the left and right PTR as a mediator.ResultA indirect effect of VS on the midline regions led to poor performance across all three cognitive domains: lexical (β = –0.077, p = 0.045), episodic (β = –0.097, p = 0.028) and executive (β = –0.079, p = 0.048). VS had a indirect effect on lexical (β = –0.097, p = 0.040) and episodic (β = –0.153, p = 0.005) tests through MD of the left PTR. Similarly, both deep (β = –0.249, p = 0.016) and periventricular (β = –0.309, p = 0.002) WMH resulted in a indirect effect on episodic memory. Only VS resulted in a negative indirect effect on episodic memory (β = –0.121, p = 0.020) through the MD of the right PTR.ConclusionHigher VS resulted in poor cognitive performance by impacting subregional volumes of the thalamus and white matter integrity of tracts. The effect of both deep and periventricular WMH on cognitive performance appears to be mediated by the white matter integrity of the left thalamic tracts more than thalamic sub‐regional volumes possibly reflecting different vascular mechanisms.
A combination of plasma phospho‐tau217 and Aβ42/40 detects early development of amyloid pathology in cognitively unimpaired individuals
Authors: Janelidze Shorena , Barthélemy Nicolas R. , Salvadó Gemma , Palmqvist Sebastian , Mattsson‐Carlgren Niklas , Ovod Vitaliy , Bollinger James G. , He Yingxin , Schindler Suzanne E. , Blennow Kaj , Stomrud Erik , Bateman Randall J. , Hansson Oskar 
Published: January 2025

AbstractBackgroundPhase 3 trials of successful anti‐amyloid therapies in Alzheimer's disease (AD) have indicated better clinical efficacy in people with less severe disease. Plasma biomarkers will be essential for efficient screening of participants in future primary prevention clinical trials testing anti‐amyloid therapies in cognitively unimpaired (CU) people with normal brain β‐amyloid (Aβ) levels by PET who are at high risk of accumulating Aβ. Here we investigated if combining plasma phospho‐tau (p‐tau)217 and Aβ42/40 could be useful to predict subsequent development of Aβ pathology in CU with normal brain Aβ by PET at baseline.MethodWe included 495 CU individuals and 339 patients with cognitive impairment (CI) from the BioFINDER‐2 study (Table). Plasma Aβ42/40 (mass spectrometry), the ratio of p‐tau217 to non‐phosphorylated tau (%p‐tau217; mass spectrometry), p‐tau231 and GFAP (immuno‐assays) were measured at baseline. Among CU participants, 224 underwent longitudinal Aβ‐PET assessments. Threshold of 40 centiloids and previously established CSF Aβ42/40 cut‐offs were applied to define Aβ‐PET and CSF Aβ status, respectively. For replication we included 249 CU individuals from BioFINDER‐1.ResultWhen detecting abnormal CSF Aβ‐status, a combination of plasma %p‐tau217 and Aβ42/40 showed better performance than individual biomarkers in CU (AUCrange=0.949, pdiff<0.024) but not in CI participants (Figure 1). Further, in CU participants with normal baseline Aβ‐PET, baseline plasma %p‐tau217 and Aβ42/40 were associated with continuous baseline Aβ‐PET measures (Figure 2) and increases in Aβ‐PET signal over time (Figure 3). Associations of plasma %p‐tau217 and Aβ42/40 with baseline Aβ‐PET (%p‐tau217: β=4.23, p<0.001; Aβ42/40: β=‐2.61, p<0.001) and longitudinal Aβ‐PET (%p‐tau217: β=0.527, p<0.001; Aβ42/40: β=‐0.259, p=0.002) were also significant in the models combining the two baseline biomarkers as predictors (Figures 2‐3). Similarly, in BioFINDER‐1, baseline plasma p‐tau217 (immuno‐assay) and Aβ42/40 were independently associated with longitudinal changes in CSF Aβ42/40 in CU participants with normal CSF Aβ42/40 at baseline. Plasma p‐tau231 and GFAP did not provide any independent value when combined with %p‐tau217and Aβ42/40.ConclusionCombining plasma measures of p‐tau217 and Aβ42/40 could be useful for predicting development of Aβ pathology in people with early stages of subthreshold Aβ accumulation. These biomarkers might thus facilitate screening of participants for future primary prevention trials.
Interpretable Deep Clustering Survival Machines for Alzheimer's Disease Risk Groups Discovery
Authors: Hou Bojian , Wen Zixuan , Bao Jingxuan , Zhang Richard , Tong Boning , Yang Shu , Wen Junhao , Cui Yuhan , Moore Jason H. , Saykin Andrew J. , Huang Heng , Thompson Paul M. , Ritchie Marylyn D. , Davatzikos Christos , Shen Li ,   Alzheimer's Disease Neuroimaging Initiative
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. Identifying different risk groups converting to AD during the mild cognitive impairment (MCI) stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies typically clustered subgroups by unsupervised learning techniques, neglecting the survival information. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which performs clustering and risk prediction simultaneously.MethodOur proposed DCSM is a hybrid survival analysis method that integrates the advantages of the discriminative and generative ideas and employ the mixture of expert distributions to fit the final survival function by maximum likelihood estimation. For the real data analysis, we use positron emission tomography (PET) from the ADNI dataset as feature. The survival information includes whether the patients convert from MCI to AD and the corresponding time. Our DCSM model predicts the risk of each patient and clusters the patients into two groups based on their risk simultaneously.ResultWe first evaluate time‐to‐event prediction performance by Concordance Index which is shown in Table 1 where our model outperforms the other baselines. Besides, we use the LogRank to evaluate the clustering performance. The difference in survival between two risk groups as shown in Kaplan‐Meier plots in Figure 2(h) is significantly larger than other baselines. Furthermore, to biologically validate our clustering findings, we carry out targeted genetic association analyses (Figure 3(a)). There are numerous targeted SNPs discovered when comparing high‐risk group to low‐risk group and normal group. Figure 3(b) illustrates the important brain regions for the high‐risk group. We highlight the top ten important regions which is consistent with the conventional brain‐AD relation findings.ConclusionOur DCSM model builds a good bridge between the imaging modality and survival information. We demonstrate the superiority of the DCSM by applying this approach to cluster patients with MCI into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of our method and characterize our clustering findings.