bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lifestyle, biological and genetic determinants of brain iron accumulation in aging
Authors: Gustavsson Jonatan , Ištvánfyová Zuzana , Falahati Farshad , Papenberg Goran , Mangialasche Francesca , Kalpouzos Grégoria 
Published: December 2023

AbstractBackgroundOptimal iron levels are crucial for brain cells’ functioning. However, brain iron overload occurs in both normal aging and neurodegenerative disorders, including Alzheimer’s disease (AD). High iron concentration triggers oxidative stress and neuroinflammation, both involved the AD pathophysiology. Despite growing evidence of the toxicity of iron overload, why iron accumulates remains unknown. A few studies have linked higher brain iron content to alcohol use, body mass index, cardiovascular health, and smoking. However, no study has investigated the factors behind iron accumulation longitudinally.MethodWe investigated the associations between potential determinants such as blood markers of iron metabolism, cardiovascular health (Framingham’s risk score; FRS), lifestyle factors (smoking, alcohol use, physical activity), ApoE (any e4) status, and iron content and iron accumulation using longitudinal data (n = 208 healthy volunteers, age = 20‐79, mean follow‐up time = 2.75 years) from the IronAge study. Iron was assessed with Magnetic Resonance Imaging using quantitative susceptibility mapping for iron‐rich or age‐sensitive regions (caudate (CA), putamen (PU), pallidum (PA), dorsolateral prefrontal cortex (DLPFC)). We used structural equation modelling to assess the relationships between determinants and iron accumulation.ResultHigher blood ferritin was related to higher iron content and iron accumulation in DLPFC (B = 0.181 and 0.173), CA (B = 0.158 and 0.225) and PU (B = 0.127 and 0.263). Higher blood haemoglobin was related to higher iron content in DLPFC (β = 0.177) and CA (β = 0.178), as well as to iron accumulation in PA (β = 0.220). Higher blood transferrin was related to lower iron content and iron accumulation in CA (B = ‐0.131 and ‐0.147) and PU (B = ‐0.160 and ‐0.232). Lower FRS was related to greater iron accumulation in DLPFC among younger adults (β = ‐0.379, age<40). Increased FRS was related to greater iron accumulation (β = 0.194). More physical activity was related to higher iron content in PA among younger adults (β = 0.231, age<40). Lastly, older (age>40) ApoE e4 carriers had higher iron content in PA (β = 0.219). No associations were found with alcohol or smoking. These results were independent of, sex, education, atrophy, white‐matter lesions, and age unless age‐stratified results.ConclusionOur study identified modifiable and non‐modifiable factors of brain iron accumulation in adulthood. The former can be targeted to prevent accelerated iron accumulation.
Influence of APOE on the Alzheimer’s disease polygenic risk scores based on hippocampal subfield volumes
Authors: Balaji Kannappan , Nijenhuis Jan , Lee Kun Ho 
Published: December 2023

AbstractBackgroundNumerous studies have shown the association between Alzheimer’s disease, hippocampal subfield volume atrophy, and the role of APOE. In our previous study, we showed the differential atrophy in the hippocampal subfield volumes (HSV) identified using Alzheimer’s disease polygenic risk scores (AD PGRS). Here, we evaluate the influence of the APOE 𝛆4 on the AD PGRS of the HSV.MethodWe calculated the AD PGRS for HSV atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). We then stratified the samples based on the APOE 𝛆4 carriers and non‐carriers. We studied the AD PGRS for the HSV for the left‐right hemispheres.ResultThe analysis was performed for the left and right hemispheres separately. The effects of APOE 𝛆4 were larger on the left hemisphere than on the right hemisphere. The effects of the APOE 𝛆4 were severe on most of the subfield volumes. Although, certain subfield volumes showed larger effects for 𝛆4 non‐carriers and certain subfield volumes showed no effect between 𝛆4 carriers and non‐carriers.ConclusionThe APOE 𝛆4 carriers showed a higher AD PGRS for hippocampal subfield volume atrophy than the APOE 𝛆4 non‐carriers. The AD PGRS and the APOE 𝛆4 showed the differential risk for the subfield volumes bilaterally. These risk scores can be used in preclinical Alzheimer’s risk modeling. We also hypothesize that hippocampal subfield volume atrophy occurs in both normal and pathological aging. However, atrophy is intense in pathological aging due to the underlying processes and the functioning of the risk genes. A clear understanding of this will enhance the specificity and sensitivity for the early diagnosis of Alzheimer’s disease.
Intravenously administered gadolinium based contrast agent as a novel approach to longitudinally measure ventricular brain clearance with magnetic resonance imaging in an Alzheimer´s disease model
Authors: Müller Luisa , Lindner Tobias , Polei Stefan , Deike‐Hofmann Katerina , Radbruch Alexander , Kuhla Angela , Teipel Stefan 
Published: December 2023

AbstractBackgroundAmyloidopathy, one pathological hallmark of Alzheimer’s disease (AD), results from dysbalanced amyloid‐β production and clearance. Therefore, disruptions in brain clearance may have a pivotal role as pathogenic drivers. Transgenic mice models of AD may serve to develop and establish minimally invasive in vivo measurement methods to observe brain clearance longitudinally.MethodWe used magnetic resonance imaging (MRI) with intravenously administered gadolinium‐based contrast agent (GBCA) on a 7T small animal ultrahighfield MRI scanner to determine brain clearance efficacy. Imaging sessions were performed longitudinally in n = 9 APPswe/PS1dE9 (tg) mice and n = 9 wild type (wt) littermates at 6, 9 and 12 months of age with a scan protocol consisting of a highly T2w Inv. Rec. RARE (FLAIR) sequence. For a subsample of n = 6 wt and n = 6 tg at 9 and 12 months we used an additional T1w RARE sequence for evaluation of parenchymal changes. Both protocols were carried out every 5‐6.5 minutes for a total of 180 minutes. We determined mean voxel values in manually delineated volumes of interest in ventricles (in T2w images) and brain parenchyma (T1w images) using PMOD software.ResultWe created intensity‐time‐curves and measured slopes for signal increase and decrease, derived from fitting with a model consisting of an exponential convoluted with another exponential and used to normalize area under the curve (AUC) values (Figure 1A‐C). Repeated measurements ANOVA revealed no differences in normalized AUC values regarding age in both lateral (p = 0.568, F(2, 28) = 0.577), 3rd (p = 0.306, F(1.903, 26.64) = 1.232) and 4th ventricles (p = 0.181, F(1.345, 18.83) = 1.924) and also no differences regarding genotype in both lateral (p = 0.684, F(1,16) = 0.172), 3rd (p = 0.737, F(1,16) = 0.117) and 4th ventricle (p = 0.186, F(1,16) = 1.910). Measurements of parenchymal clearance capacity are still ongoing and first results will be presented at the conference.ConclusionOur first results showed no measurable difference in the brain clearance efficacy. This could have two possible reasons. Either ventricular clearance measurements are not sensitive enough to measure AD related changes in brain clearance or the used pathogenetic model shows no changes in clearance efficacy. Our still ongoing analyses of parenchymal brain could help to elucidate further on this question.
Seed‐competent, high‐molecular‐weight Aβ species is abundantly extracted in the cerebrovasculature and meninges in Alzheimer’s disease
Authors: Uchigami Hirokazu , Kashiwagi‐Hakozaki Mayu , Matsubara Tomoyasu , Murayama Shigeo , Saito Yuko , Toda Tatsushi , Hashimoto Tadafumi , Iwatsubo Takeshi 
Published: December 2023

AbstractBackgroundSoluble high‐molecular‐weight (HMW) amyloid β (Aβ) species extracted from autopsied brains of patients with Alzheimer’s disease (AD) have been demonstrated to exhibit a seeding potency to induce cerebral β‐amyloidosis. However, it remains unknown where Aβ seed species resides and how it propagates throughout human AD brains. Our objective is to detect Aβ seed in human AD brain cerebrovasculature and meninges that are enriched with cerebral amyloid angiopathy (CAA).MethodWe examined eight autopsied human AD brains with abundant CAA and eight AD brains without immunohistochemically detectable CAA. Tris‐buffered saline (TBS)‐soluble fractions from the brain parenchyma or meningeal tissues were separated by size‐exclusion chromatography (SEC) with Superdex 75 column, and the concentrations of Aβ in each SEC‐separated fraction were measured by enzyme‐linked immunosorbent assay (ELISA). The TBS‐soluble, high‐molecular‐weight Aβ‐positive fractions were stereotaxically injected into the unilateral hippocampus or cisterna magna of 3‐month‐old amyloid precursor protein (APP) transgenic (tg) mice, and Aβ deposition in the hippocampus was assessed after four months.ResultHMW Aβ species were detected in TBS‐soluble fractions of the meninges and cerebrovasculature isolated from human AD brains. The concentration of HMW Aβ species extracted from meninges and cerebrovasculature was significantly higher than that from brain parenchyma in the same cases. Meningeal HMW Aβ species induced a robust Aβ deposition in the hippocampus, the area of which was significantly higher than that from the same weight of brain parenchyma. Moreover, the concentration of HMW Aβ species in CAA‐rich AD brains with meninges removed was significantly higher than those of CAA‐minimal AD brains. Intracisternal injection of HMW Aβ species derived from human AD brains induced not only CAA but also Aβ plaque.ConclusionSeed competent Aβ species are more abundant in cerebrovasculature and meninges than in brain parenchyma, and may accumulate in CAA in human AD brains. The possibility that HMW Aβ species in CAA contributes to Aβ plaque formation warrants further investigation in future studies.
Combining speech biomarkers for cognition and mood improves diagnostic accuracy between MCI and subjective cognitive decliners in a memory clinic sample
Authors: Ramakers Inez H.G.B. , ter Huurne Daphne B.G. , König Alexandra , Mallick Elisa , Linz Nicklas , Tröger Johannes 
Published: December 2023

AbstractIntroductionDepressive symptoms are a common comorbidity in an old age memory clinic population. Cognitive and affective syndromes coexist, and supposedly modulate each other. While, there have been separate research streams on analysing speech to infer cognitive performance, as well as mood related symptoms, they are seldom investigated side‐by‐side. We present results from a small sub sample of a larger MCI and dementia cohort indicating that modelling both depressive symptoms and cognitive problems from speech can improve the diagnostic accuracy for MCI above speech biomarkers for cognition alone.MethodsThe ki:e Speech Biomarker for cognition (ki:e SB‐C; Tröger et al., 2022) and a free speech task were collected from 35 memory clinic participants (12F) with 10 of them having an MCI diagnosis (compare Table 1). From the free speech tasks (“Tell me about your upcoming vacation plans.”) we extracted speech features using ki:elements proprietary speech analysis engine SIGMA. Free speech features were corrected for cognitive performance, sex and age and then combined into a mood speech biomarker. To benchmark the diagnostic utility of both cognitive and mood speech biomarker side by side, we trained two groups of simple classifiers (tree based classifiers with leave one participant out cross validation) once with SB‐C alone and once with both SB‐C and the newly‐composed mood speech biomarker.ResultsBy combining the speech biomarker for cognition and mood diagnostic performance was significantly improved (AUC of 88%) as compared to classification with the SB‐C alone (AUC of 74%). The mood biomarker correlates significantly with the Geriatric Depression Scale (r = 0.39, p < 0.05). Most important free speech features were jitter, number or pauses, mean pause duration or pronoun rate.ConclusionOverall we present rare evidences that combining speech‐based digital biomarkers tapping both into cognition but also mood‐related symptoms helps to improve diagnostic accuracy in MCI patients. Though just a small feasibility study, the results are clearly encouraging and point towards potential application in both clinical practice as well as clinical research.
References: Tröger, J., et al. (2022). Digital biomarkers,30;6(3):107‐116. https://doi.org/10.1159/000526471.
Levels of Mitophagy Biomarkers Differ Between Individuals With Alzheimer’s disease and Frontotemporal Lobar Degeneration
Authors: Katonova Alzbeta , Veverova Katerina , Horakova Hana , Angelucci Francesco , Laczó Jan , Hort Jakub , Shi Liu , Fang Evandro Fei , Vyhnalek Martin 
Published: December 2023

AbstractBackgroundReduced removal of dysfunctional mitochondria has been implicated in the pathogenesis of many neurodegenerative diseases, including Alzheimer’s disease (AD) and Frontotemporal lobar degeneration (FTLD). Mitophagy is the main cellular pathway through which aberrant and aged mitochondria are removed. Impaired neuronal mitophagy triggers the accumulation of damaged mitochondria, leading to cell death and neurodegeneration. While recent evidence suggests that levels of various mitophagy‐associated proteins are altered in AD, whether and in which direction mitophagy biomarkers are altered in non‐AD pathologies remains unclear. Therefore, we aimed to compare the levels of mitophagy markers between biomarker‐defined patients in various stages of AD and FTLD and cognitively unimpaired individuals (CU).MethodWe included 253 biomarker‐defined individuals from Czech Brain Aging Study: 86 AD dementia (ADD), 86 mild cognitive impairment due to AD (AD‐MCI) 21 FTLD dementia (FTLDD), 21 MCI due to FTLD (FTLD‐MCI), and 39 CU individuals. They underwent clinical examination, complex neuropsychological assessment, and brain MRI. Commercial ELISA kits were used to measure standard AD biomarkers in CSF (Aß42, Aß40, pTau, tTau), and mitophagy biomarkers in CSF (ULK1‐ autophagy factor essential for mitophagy initiation) and in serum (TFEB‐ transcription factor critical for mitophagic flux).ResultWe found a significant increase in ULK1 CSF levels in MCI‐FTLD compared to MCI‐AD (p<.01), ADD (p<.001), and CU (p<.01). There was also a significant increase in TFEB serum levels in FTLDD compared to MCI‐AD (p<.01) and CU (p<.01). Similarly, TFEB serum levels were elevated in FTLD‐MCI compared to ADD (p<.001), but were not elevated compared to CU.ConclusionThere is an upregulation in the key mitophagy activator (ULK1) and in the mitochondrial quality control protein (TFEB) in FTLD compared to AD in various stages of disease (MCI vs dementia). Our data suggest that mitophagy plays different roles in the sub‐types of neurocognitive disorders, with a significant increase in the levels of mitophagy markers in patients with FTLD compared to healthy controls and patients with AD. Further research is needed to elucidate the role of mitophagy in the pathophysiology of different sub‐types of neurocognitive disorders.
Concept for the combination of blood and digital markers: ideas from the PROSPECT‐AD study
Authors: König Alexandra , Linz Nicklas 
Published: December 2023

AbstractWhile knowledge about dementia and its causes is progressing rapidly, it remains challenging in clinical practice to detect accurately cognitive decline in the early stages of neurodegenerative diseases such as Alzheimer’s disease (AD). Improving the identification of early changes in the population is a prerequisite for dementia prevention and offering future disease‐modifying treatments to individuals most likely to benefit. PET and CSF biomarkers are the most widely used biomarkers to aid diagnosis in clinical research but present major limitations for clinical practice (i.e., cost, accessibility).Thus, the importance to find cost‐effective, and easily accessible tools for early detection has resulted in the development of innovative solutions that are specifically geared towards facilitating the population wide screening of at‐risk individuals.Emerging blood‐based markers have the potential for widespread clinical use, and could facilitate timely diagnosis. However, several limitations must be overcome such as finding standardized techniques for measurements of blood biomarkers, establishing reliable cutoff values for individual biomarkers, or addressing the impact of confounding factors.Recently speech analysis has become mature enough to automate speech‐based testing procedures and capture clinically fine‐grained features that are highly sensitive to early cognitive changes. Hence, speech can be utilized to indicate potential candidates to advance in the screening funnel for an on‐site more comprehensive assessment stage.Supplementing the assessment of subjective complaints by automatized speech based analysis of cognitive tests and blood‐based AD biomarkers may offer a feasible approach for effective and cost‐efficient community‐based screening programs.PROSPECT‐AD aims to scale up and to validate a novel, digital, and solely speech‐based biomarker extracted from an ordinary telephone to enable remote screening of early AD subjects. Within the PROSPECT‐AD project, novel speech features extracted during remote cognitive testing, and blood AD biomarker measurements will be compared to gold standard AD biomarker such as CSF‐amyloid‐β 1‐42 and CSF p‐Tau to assess value of a combinational approach. For this, the project collaborates with already ongoing cohorts such as EPAD (UK), DESCRIBE and DELCODE (Germany), BioFINDER Primary Care (Sweden) and Beta‐AARC (Spain) by adding the collection of speech data to existing protocols or as follow‐up assessments over the telephone.
Decreased Retinal Microvasculature in Mild Cognitive Impairment: An Optical Coherence Tomography Angiography Study
Authors: Velavarajan Goutham , Mallikarjun Divya N , M L Abhishek , Narayanasamy Rajitha , Malo Palash Kumar , Hameed Shafeeq K Shahul , Stezin Albert , R Meghana , Menon Meenakshi , Partha Ajith , C M Amitha , Singh Sadhana , HS Sunitha , Arvind Prathima , Kommaddi Reddy Peera , Diwakar Latha , Sundarakumar Jonas S. , Issac Thomas Gregor 
Published: December 2023

AbstractBackgroundEye are the extension of brain, any vascular abnormalities in the brain might reflect in ocular structures. The retinal microcirculation of an individual is similar to the small blood vessels found in the brain. The changes to the retina that result from neurodegenerative diseases can be observed through the eye. Studies have shown reduced retinal thickness in MCI when compared to healthy controls (HC). Therefore, OCT plays an important role in neurodegenerative diseases, and also in the MCI (Dudekova et al., 2016) (Sefton et al., 2022).MethodThis is a case‐control study. Total of 19 eyes from 11 MCI subjects, and 20 eyes from 15 HC were taken for analysis from Tata Longitudinal Study on Aging (TLSA) cohort (Sundarakumar et al., 2020). Clinical dementia rating (CDR) score was used to group the participants as having normal cognition and MCI. A score of 0 and 0.5 was considered as subjects having normal cognition and MCI respectively. Macular and radial peripapillary angiography were done using SD OCT‐A to compare foveal avascular zone (FAZ), Superficial capillary plexus (SCP) vessel density VD and SCP perfusion density (PD), and radial peripapillary capillary perfusion (CPD) and radial peripapillary capillary flux index (CFI) between both the MCI and HC groups.ResultTable 1 shows demographic details, and Table 2 shows comparison of SD OCT‐A data between MCI and HC. MCI subjects showed significant decrease in mean CPD compared to HC (44.19% ± 1.59% vs 45.05% ± 2.03%; p < 0.05). Also, MCI group showed significant decrease in mean CFI (0.42 ± 0.03 vs 0.43 ± 0.02; p <0.05) compared to HC.ConclusionWe demonstrated that there is decreased radial peripapillary perfusion and flux index in MCI subjects. By using SD OCT‐A, microvascular changes in early stage of dementia offers as a new way of detecting ocular biomarkers.
Is a dementia diagnosis predictive for palliative care? ‐ A machine learning approach
Authors: Rakusa Elena , Reinke Constantin , Doblhammer Gabriele , Schmid Matthias , Welchowski Thomas 
Published: December 2023

AbstractBackgroundPalliative care (PC) aims to provide dignified and self‐determined life for dying individuals. While the integration of PC for tumor diseases in health care is established in case of progressive course and unfavorable prognosis, PC is surprisingly not mentioned in the German S3‐guideline for dementia. PC is mainly used by older people, with proximity to death rather than age being the determining factor. However, more people are dying at older ages. Moreover, as the elderly population increases, the number of dementia patients also increases. The aim of this paper is to examine the utilization of PC at the end of life and to explore whether a dementia diagnosis is among the top factors that predict PC utilization.MethodWe developed predictive models using discrete survival conditional inference trees (ctrees) and discrete survival conditional inference forests (cforests). We used randomly drawn claims data from a German health insurer from 2014 aged 50+ (N = 250.000), and followed them quarterly until 2019. We analyzed the last eight quarters of life of 13,628 individuals who had died in the observation period and used outpatient (OPC) and inpatient (IPC) PC as outcomes. We included medical history, care measures, medications, comorbidities and patient status (diagnosis of dementia or cancer, combinations of both diagnoses, and diagnosis‐free patients) as possible predictors. Dementia was defined by ICD‐10 codes F00‐F03,F05.1,G23.1,G30,G31.0 and G31.82 and cancer by C00‐C26,C30‐C41,C45‐C58,C60‐C97 and D00‐D09. For evaluation we used concordance‐index (C‐Index) and calibration plots. We identified the most important predictors by using a permutation variable importance approach.ResultThe models for OPC and IPC had a discriminatory power of more than 0.7 (C‐Index) on the test data. The calibration plots showed well calibrated ctrees, but the cforests require further tuning (Figure 1). Among the top 20 predictors of OPC were proximity to death as well as cancer and dementia diagnoses, and their combinations. Neither cancer nor dementia diagnoses were important predictors of IPC.ConclusionWe found that a dementia diagnosis has comparably predictive power for OPC as a cancer diagnosis. This issue should be addressed by including appropriate measures in dementia care guidelines.