bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modifiable Risk Factors and Explainable Machine Learning Model for Predicting Hospitalization Outcomes in Geriatric Patients with Dementia
Authors: Wang Xin 
Published: December 2022

AbstractBackgroundGeriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. There is an urgent need to identify modifiable risk factors and develop predictive models for hospitalization outcomes in order to mitigate risks early on during hospitalization, thus improving outcomes and the quality of life in these patients.MethodOver 150 clinical and demographic factors of 15,678 elderly patient encounters and 8,407 distinct elderly patients with dementia (January 2010 – December 2019) were retrieved from our hospital clinical data warehouse. We categorized demented geriatric patients into four categories: Alzheimer’s disease dementia, with 4,993 patients encounters; vascular dementia, with 3,735 encounters; Parkinson’s disease with dementia, with 4,173 encounters; and other unspecified dementias, with 2,777 encounters. We narrowed the risk factors down to twenty using analysis of variance (ANOVA) and Glmnet regressive selection. We developed a stacking ensemble tree model using the twenty engineered features to predict hospitalization outcomes defined by hospital stays and discharge dispositions, followed by implementing SHAP (Shapley Additive Explanations) explainable artificial intelligence algorithm to increase the model transparency by ranking the importance of the risk factors.ResultOur stacking ensemble learning model achieved a cross‐validated performance AUC of 0.757 (95% CI, 0.752‐0.762) on patients with Alzheimer’s disease, 0.658 (95% CI, 0.656‐0.660) on vascular dementia, and 0.664 (95% CI, 0.660‐0.668) on Parkinson’s disease with dementia. The model outperforms the prevalent baseline models, including logistic regression and multilayer perceptron neural networks in identifying hospitalization outcomes of demented geriatric patients across the patient cohorts with Alzheimer’s disease, vascular dementia, Parkinson’s disease with dementia, and other unspecified dementias.ConclusionIn this study, our explainable machine learning model achieved good accuracy across the four types of dementia patients with well‐defined risk factors. This predictive model will be invaluable in allocating scarce hospital resource for timely interventions on high‐risk demented geriatric patients. Our ongoing study is investigating lateral associations between the risk factors across the four types of dementia, focusing on the modifiable risk factors that can be implemented upon admission and designing a prospective randomized study for hospitalized geriatric patients with cognitive decline.
Associations between the glial marker myo‐inositol measured by 7T MRS and other AD‐relevant measures
Authors: Göschel Laura , Fillmer Ariane , Dell'Orco Andrea , Melin Jeanette , Aydin Semiha , Kurz Lea , Riemann Layla Tabea , Wang Hui , Ittermann Bernd , Rujescu Dan , Pendrill Leslie , Köbe Theresa , Flöel Agnes 
Published: December 2022

AbstractBackgroundMyo‐inositol (MI) is a presumed marker for glial activation which can be quantified by non‐invasive Magnetic Resonance Spectroscopy (MRS) and is suspected to be elevated in early Alzheimer’s disease (AD). In the framework of the NeuroMET2 project, we investigated the relationship between MI and other AD‐relevant measures.MethodsAbsolute concentrations of MI were measured by 7 tesla MRS in the posterior cingulate cortex (PCC)/precuneus of 26 cognitively healthy individuals (HC), 23 patients with subjective cognitive decline (SCD), 23 with mild cognitive impairment (MCI) and 24 with dementia due to suspected AD. Using linear models, MI’s association with memory ability (NeuroMET Memory Metric), volumes of the hippocampus and PCC/precuneus, and seed‐based functional connectivity were investigated. Interaction terms involving the apolipoprotein E (APOE) ε4 allele were analyzed additionally. All models were adjusted to age and education and weighted based on Cramér‐Lao lower bounds.ResultsAbsolute MI concentrations were substantially elevated in AD patients (adjusted mean [95% CI]=8.5 mmol/l [7.8; 9.3]). However, there were no relevant differences between SCD (6.7 mmol/l [6.0; 7.3]) or MCI (7.5 mmol/l [6.8; 8.2]) and participants of the HC group (7.2 mmol/l [6.6, 7.9]). Across the whole cohort, higher levels of MI were associated with lower memory ability (ß [95% CI]=‐0.31 [‐0.44; ‐0.17], std. ß=‐0.40, p<0.001), smaller hippocampus volume (ß=‐59.89 [‐131.40; 11.62], std. ß=‐0.16, p=0.100) and lower seed‐based functional connectivity (ß=‐3948.41 [‐6480.69; ‐1416.13], std. ß=‐0.34, p=0.003). There was no relevant effect between MI and PCC/precuneus volume (ß=83.27 [‐117.89; 284.43], std. ß=0.08, p=0.413). Finally, there was no substantial mediation effect of APOE ε4 carriership on the association between MI and neither memory ability (ß=0.09 [‐0.18; 0.36], std. ß=0.37, p=0.519), hippocampus volume (ß=‐55.60 [‐223.48; 112.29], std. ß=‐0.45, p=0.511), PCC/precuneus volume (ß=‐110.68 [‐546.22; 324.86], std. ß=‐0.32, p=0.614) nor seed‐based functional connectivity (ß=2009.46 [‐4207.40; 8226.32], std. ß=0.55, p=0.520).ConclusionsAcross a cohort with participants ranging from healthy to AD, we showed that glial activation, measured by MI concentration, was associated with key AD‐related processes. The potential of MI as a non‐invasive biomarker for increasing memory impairment in pre‐dementia stages remains to be explored in longitudinal studies.
Passive Digital Markers for Alzheimer’s Disease and Related Dementia Predict Mild Cognitive Impairment
Authors: Abbas Mostafa , Morland Thomas , Lichtenstein Maya , Milad Zina Ben , Finney Glen , Boustani Malaz , El‐Manzalawy Yasser 
Published: December 2022

AbstractBackgroundAlzheimer’s Disease and Related Dementia (ADRD) typically progresses through three clinical stages: Normal Cognition (NC), Mild Cognitive Impairment (MCI), and ADRD. Early detection of MCI is critical for identifying individuals at high risk for ADRD for proper management and early interventions to prevent progression to ADRD. Recently, Boustani et al. identified a passive signature in unstructured Electronic Medical Records (EMR) data for early detection of ADRD. Our objective is to test whether machine learning models trained using ADRD Passive Digital Markers (PDMs) can accurately predict MCI 3 years in advance.MethodsUsing a dataset of 1379 and 5516 MCI and healthy individuals (respectively), we represented each individual using 209 markers in the form of the counts of specific words or phrases (e.g., infection, respiration disorders, anti‐anxiety agents) extracted from clinical notes. In addition, we also included three demographic variables (age at prediction time, gender, and race). Each patient was assigned an index date, where the index date for MCI patients is the MCI diagnosis date. Each MCI patient has four distinct controls with adjusted age at their MCI index date. The prediction time is set to index date – k, where k equals 1 and 3 years. The PDMs were extracted from the EMR data collected within two years before the prediction date. Four widely used machine learning algorithms were evaluated using 10‐fold cross‐validation procedures for predicting MCI 1‐year and 3‐years prior. The predictive performance was assessed using ROC curves and Area Under ROC Curve (AUC).Results
Figure 1 shows that machine learning models developed using ensemble 200 decision trees trained using the gradient boosting algorithm outperform the remaining three algorithms evaluated in this study. The best AUC scores are 0.84 and 0.78 for identifying MCI patients 1 year and 3 years in advance (respectively). Table 1 summarizes the top 30 PDMs and their feature importance scores for predicting 1‐year and 3‐year MCI.ConclusionMachine learning models trained using PDMs, originally designed for early detection of ADRD, can also predict MCI. Future work will leverage structured EMR data to improve the performance of our models.
Machine learning assisted neuropathological assessment of 126 brains from PSEN1 E280A familial Alzheimer’s disease
Authors: Villalba‐Moreno Nelson David , Littau Tim , Littau Jessica Lisa , Mejia‐Cupajita Juan Pablo , Hartmann Kristin , Cardona‐Madrigal Duvan , Krasemann Susanne , Glatzel Markus , Lopera Francisco , Villegas‐Lanau Andres , Sepulveda‐Falla Diego 
Published: December 2022

AbstractBackgroundDuring the last 25 years we have collected 126 brains from the largest family in the world carrying PSEN1 mutation E280A and suffering from familial Alzheimer’s disease (FAD). Classical neuropathology scales rate all FAD cases as severe, overlooking the degree of heterogeneity observed in these cases. Here we present a new approach involving High Throughput Digital Neuropathology (HTDNp), Machine Learning (ML) classification of pathology, and multidimensional data analysis.MethodFrontal, Temporal, Parietal, and Occipital cortices, and cerebellum of all 126 brains were processed for H&E staining and immunohistochemistry (IHC) for amyloid beta (Ab) and hyperphosphorylated tau (pTau). Cases were diagnosed, and staged according to CERAD, Braak and Thal systems. All slides were digitally scanned and processed for ML classification. Ab pathology was classified as core and diffuse plaques, and cerebral amyloid angiopathy (CAA), using a previously trained convolutional neural network (CNN). pTau pathology was classified as neurofibrillary tangles, dystrophic neurites, and diffuse pathology, by training a new CNN. ML was used for data dimensionality reduction, using self‐organizing maps (SOM). Clinical profiles were identified in the pathological clusters, analyzing presence and onset of specific symptoms.ResultPSEN1 E280A cases showed high degree of heterogeneity regarding ages of onset, disease duration, brain weight, and ApoE haplotype. Clinically, memory and language impairment were prevalent, while depression, insomnia, gait disorders and behavioral symptoms presented only in some cases. All cases were classified as CERAD C, Thal 5, and Braak VI. ML‐assisted classification and quantification of Ab and pTau pathology showed a high degree of variability among patients and brain areas. SOM analysis identified four distinct pathological clusters, each one with a characteristic clinical profile, indicating a clinical correlate for FAD pathological presentation in these cases.ConclusionCurrent neuropathological diagnosis of Alzheimer’s disease is not sensitive enough to differentiate among disease subtypes or heterogeneity. This can be detected by using HTDNp and ML tools, allowing the identification of clinically relevant pathological subtypes even in PSEN1 E280A brains, an otherwise uniform sample. This approach can be explored further in other FAD and sporadic Alzheimer’s disease cases, aiming for a more precise diagnosis, and a better understanding of the disease.
Amyloidogenic amylin deposits on red blood cells of stroke patients
Authors: Leibold Noah S , Kotiya Deepak , Despa Florin , Sheikhi Lila , III David L Dornbos , Pahwa Shivani S , Trout Amanda , Frank Jacqueline A , Pennypacker Keith R , Goldstein Larry B , Fraser Justin F 
Published: December 2022

AbstractBackgroundAmylin, a 37‐residue amyloidogenic peptide, is co‐secreted with insulin by pancreatic beta‐cells (Verma et al. 2020). Vascular deposition of amylin has been noted in patients with diabetes and neurocognitive disorders (Ly et al., 2017) Emergent large vessel occlusions (ELVOs) result in severe ischemic strokes without appropriate treatment. Growth factors, including erythropoietin (EPO), are involved in post‐stroke recovery as a potential neuroprotectant target, inhibiting apoptosis and decreasing inflammation (Jerndal et al., 2010). Here, amylin accumulation in plasma, red blood cells (RBCs), and ELVOs (where available) and plasma EPO levels were compared between stroke and control patients. Due to amylin’s amyloidogenic propensity, we hypothesized that there would be an association between amylin uptake and stroke incidence.MethodA prospective registry (Blood and Clot Thrombectomy Registry and Collaboration; BACTRAC) was developed to analyze blood and thrombus directly in patients presenting with an ELVO. Total protein concentration and amylin concentrations of clot lysates, plasma, and RBC lysates were obtained via bicinchoninic acid assay (BCA) and competitive enzyme‐linked immunosorbent assay (ELISA), respectively. Plasma EPO concentrations were obtained via ELISA. An amylin uptake coefficient (the level of amylin in RBC lysates divided by the sum of amylin present in RBC lysates and plasma) was calculated to determine the degree to which circulating amylin is depositing on RBCs.ResultDue to a significant difference between control and stroke RBCs (p = 0.0012, Figure 1) in total protein concentration, all downstream analyses were normalized to respective protein concentrations. A significant difference in amylin uptake by RBCs between stroke and control patients is shown in Figure 2 (p = 0.0073). An uptake coefficient greater than 0.50 indicates uptake of amylin by RBCs. Finally, a significant difference in plasma EPO concentration was observed between stroke and control (p = 0.0017; Figure 3).ConclusionConsistent with our hypotheses, the present data indicates that amylin uptake by RBCs is significantly increased in stroke patients than in control patients. However, both groups show evidence of accumulation, as indicated by uptake coefficients greater than 0.50. Further, levels of erythropoietin are significantly increased in stroke patients. Further investigation into whether amylin may be thrombogenic is warranted.
Subcutaneous Dose Selection of Lecanemab for Treatment of Subjects with Early Alzheimer’s Disease (EAD)
Authors: Hayato Seiichi , Rawal Sumit , Takenaka Osamu , Landry Ishani , Boyd Peter , Aluri Jagadeesh , Willis Brian A , Swanson Chad J , Yasuda Sanae , Hussein Ziad , Reyderman Larisa 
Published: December 2022

AbstractBackgroundLecanemab is a humanized IgG1 monoclonal antibody that preferentially targets soluble aggregated Aβ species (protofibrils) with activity at insoluble fibrils. Lecanemab 10 mg/kg IV biweekly dose (10BW) is being evaluated in the Clarity AD Phase 3 study in Early AD. A Phase 1 study was conducted to assess the absolute bioavailability (BA) of subcutaneous (SC) administration relative to IV infusion. Modeling and simulation analysis was conducted to demonstrate equivalence of a fixed weekly SC dose to body weight‐based IV 10BW dose with regard to lecanemab exposure, safety and efficacy.MethodBased on estimates of BA and absorption rate constant from Phase 1 and the population PK model describing lecanemab PK after IV infusion, estimates of the area under the concentration‐time curve (AUC) and maximum concentration (Cmax) at steady‐state were generated for the proposed fixed SC dose and IV 10BW. Exposure‐response simulations were conducted to compare positron emission tomography (PET) standard uptake ratio (SUVr)as a measure of pharmacodynamic effect, and incidence of amyloid related imaging abnormality‐E (ARIA‐E) as a measure of safety between SC and IV doses. The impact of body weight on the lecanemab exposure, reduction in amyloid PET SUVr and ARIA‐E risk when administered as a fixed SC dose and body weight‐based IV dose were explored.ResultPhase 1 data support a proposed fixed lecanemab SC dose of 720 mg administered weekly (720W). PK simulations demonstrate that the AUC of SC 720W were similar to IV 10BW. Exposure‐response simulations demonstrate comparable reduction in amyloid PET SUVr following SC 720W and IV 10BW, and lower incidence of ARIA‐E in SC 720W due to lower Cmax following SC administration. Lecanemab exposure differences between a fixed SC dose and body weight‐based IV dose were observed at the extremes of body weight; however, this did not have a meaningful effect on the reduction in PET SUVr and ARIA‐E risk.ConclusionThis analysis demonstrates that the proposed SC 720W results in equivalent AUC predicted to attain a comparable reduction in amyloid PET SUVr and a lower Cmax predicted to produce a lower incidence of ARIA‐E compared to IV 10BW.
Multi‐omics integration via similarity network fusion to detect subtypes of aging
Authors: Yang Mu , Matan‐Lithwick Stuart A , Wang Yanling , De Jager Philip L , Bennett David A , Felsky Daniel 
Published: December 2022

AbstractBackgroundMolecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer’s disease (AD). However, existing subtyping studies have mostly focused on single data modalities, such as RNA sequencing (RNAseq), which provide incomplete neurobiological information on pathological processes. To remedy this, we applied similarity Network Fusion (SNF), a method capable of integrating multiple high‐dimensional multi‐‘omics data modalities simultaneously.MethodWe analyzed human frontal cortex brain tissue samples characterized by five ‘omic modalities bulk RNAseq (18,629 genes), DNA methylation (53,932 cpg sites), histone H3K9 acetylation (26,384 peaks), tandem mass tag proteomics (7,737 proteins), and metabolomics (654 metabolites). SNF followed by spectral clustering was used for subtype detection, with subtype numbers determined by eigen‐gaps and dip‐test statistics. Normalized Mutual Information (NMI) was calculated to determine the contribution of each modality and feature to the fused network. Resulting subtypes were characterized by associations with 12 age‐related neuropathologies and cognitive performance.ResultFusion of all five data modalities (overlapping n = 111) yielded four molecular subtypes (nS1 = 32, nS2 = 26, nS3 = 31, nS4 = 22); S1 exhibited lower episodic memory performance than other subtypes proximal to death (t = 4.7, p = 1.5×10‐4). Histone acetylation (NMI = 0.32) and RNAseq (NMI = 0.16) contributed most strongly to this fused network; the top individual features were an acetylation peak in the promoter of CD200 and RNA abundance of PARP4. Secondary analysis fusing only RNAseq and histone acetylation (n = 520) yielded five subtypes which were correlated with the fully integrated subtypes (Fisher’s p = 5.0×10‐4) and strongly associated with AD neuropathology and episodic and semantic memory. Sensitivity analyses of all modality combinations and sample subsets found substantial influences of sample size and subtype number, but reinforced the importance of histone acetylation, RNAseq, and DNA methylation.ConclusionWe identified highly integrative molecular subtypes of aging derived from up to five multi‐‘omics data modalities simultaneously. These subtypes recapitulate some features of previous subtyping work in AD using single modalities, but also provide new molecular targets and shed light on the benefits and challenges of multi‐omic integration and individual subtyping in this field.
Functional Connectivity in Hippocampal Sub‐Regions as a Function of Age, Sex, and ApoE e4 Status
Authors: Frank Conner , Albertazzi Abigail , Cruz Alan De La , Robinson Elizabeth , Lewis Jenna , Chavarin Dalia , Murphy Claire 
Published: December 2022

AbstractBackgroundOlfactory disturbances, thought to be related to preclinical neuropathology and functional decline of the medial temporal lobe, are a reliable predictor of later Alzheimer’s Disease (AD) onset (Murphy, 2019). This study focused on the relationship between hippocampal functional connectivity, olfaction, and risk factors for AD by studying the sub‐regions of the hippocampus independently.MethodFunctional and structural MRIs were obtained at 3T from 29 non‐demented older adults, genotyped for ApoE allele status. Archival functional data collected during correct recognition of verbal labels for odors presented prior to scanning were analyzed. Seed‐to‐voxel FC was calculated for six regions of interest (ROIs), including the dorsal and ventral dentate gyrus (dDG and vDG), subiculum, and CA fields 1‐3. The relationship between FC and age, ApoE, and sex was assessed using multiple linear regression.ResultIn the left vDG, decreased FC with the precuneus was observed in e4 positive older adults, while anterior cingulate gyrus FC increases were associated with the interaction of age, gender, and ApoE e4 status. In the right vDG, the presence of an ApoE e4 allele and increasing age were associated with decreased FC with the right anterior precuneus and increased FC with the right posterior precuneus, whereas there was a negative interaction effect between ApoE e4 and age in the posterior precuneus and a positive interaction effect in the anterior precuneus.ConclusionDifferential FC with the anterior precuneus, involved in self‐referential processing, and posterior precuneus, normally involved in successful memory retrieval, suggests that early risk factors result in greater reliance on posterior parietal processing, with the interaction effect implying that genetic risk and increasing age may result in FC patterns reflective of precuneus connectivity in preclinical AD (Cavanna & Trimble, 2006; Klaassens et al., 2017). The vDG may be of special importance because it is associated with odor memory processing (Kesner, 2018). Further research should focus on hippocampal sub‐region FC during olfactory processing.Supported by NIH grant # AG062006‐04 to CM. Special thanks to Aaron Jacobson, Lori Haase, Erin Green, MiRan Wang, and Vincent Rupp for data collection efforts.
Absolute Bioavailability of a Single, Fixed Subcutaneous Dose of Lecanemab in Healthy Subjects
Authors: Rawal Sumit , Duong Anh , Landry Ishani , Aluri Jagadeesh , Boyd Peter , Yagi Takuya , Swanson Chad J , Reyderman Larisa 
Published: December 2022

AbstractBackgroundLecanemab is a humanized IgG1 monoclonal antibody that preferentially targets soluble aggregated Aβ species (protofibrils) with activity at insoluble fibrils. A subcutaneous (SC) formulation is in development as an alternative to intravenous (IV) infusion. The present study evaluated the absolute bioavailability (BA), pharmacokinetics (PK), safety and immunogenicity of lecanemab following a single fixed 700 mg SC dose.MethodsThis was an open‐label, parallel‐group study conducted in healthy subjects that were randomized into IV or SC dose group; 5 Japanese subjects were randomized to the SC dose group. Serum samples for determination of lecanemab PK parameters were collected after single dose of 10 mg/kg IV infused over approximately 1 hour or fixed 700 mg SC injection in the abdomen. The absolute BA was determined by the ratio of dose normalized area under the curve from zero to infinity (AUC(0‐inf)) for SC versus IV dosing.ResultsThirty subjects received IV infusion and 29 subjects received SC injection. After SC dosing, maximum concentration was observed 72 hours post dose and was 4‐fold lower compared to IV infusion, which reflected the long absorption phase following SC administration compared with IV infusion. The AUC was 2‐fold lower for SC administration compared with IV infusion. Lecanemab half‐life (∼ 7 days) was similar following SC and IV administration. The absolute BA following a single SC 700 mg dose of lecanemab was 49.7% (90% CI: 43.5 – 56.8). PK of lecanemab after SC administration in Japanese subjects was similar to those in non‐Japanese subjects. Mild to moderate injection site reactions were experienced by 6 (20.7%) subjects in the SC dose group. Grade 1 and 2 infusion related reactions were experienced by 10 (33.3%) subjects in the IV dose group. One subject in IV and 2 in SC dose group, had confirmed treatment‐emergent positive anti‐drug antibodies (ADA), with low titers ranging from 16 to 32 with no subject being positive for neutralizing antibodies.ConclusionLecanemab was well tolerated following both SC and IV administrations. The estimate of absolute BA of SC compared to IV dosing will be used to define SC dose and dosing regimen equivalent to IV dosing.